Wildfire is a severe natural disaster that poses a significant threat to the natural environment, as well as the safety of human life and property. The timely detection of wildfires plays a critical role in minimizing their detrimental impact. Himawari-8, a geostationary satellite equipped with an advanced Himawari imager (AHI) sensor, can provide full-disk data every 10 min, thus enabling near real-time and large-scale monitoring of wildfires. In this article, a wildfire detection method based on the spatiotemporal features of Himawari-8 data is proposed. First, a temporal convolutional network (TCN) is employed to predict the brightness temperature of the bands related to wildfire detection, achieving prediction results with a mean absolute error (MAE) of 0.28 K, a mean square error (MSE) of 0.30 K2, and a mean absolute percentage error (MAPE) of 0.10%. Then, various feature strategies are devised from spectral, spatial, and temporal aspects, and machine learning models are utilized for wildfire detection research. Among the considered strategies, strategy 4, which integrates spectral, spatial, and temporal features with the random forest (RF) algorithm, exhibits the most effective wildfire detection performance. It achieves a precision of 0.62, an omission of 0.34, and an F1-score of 0.64. Compared with the threshold method, precision increased by 0.05, omission decreased by 0.31, and F1-score increased by 0.21. To further evaluate practical applicability, the combination of strategy 4 and the RF is employed for wildfire detection near power grid transmission lines. In this scenario, out of the 295 real wildfires, 253 are successfully detected, resulting in a recall of 0.86. These experimental results affirm the effectiveness of the proposed method for wildfire detection.

Landslide natural disasters (LND) have high frequency, wide distribution, and multiple occurrences, causing significant losses to personal and property safety. LNDs account for over 70% of natural geological disasters in China, often caused by precipitation. Xingguo County, Jiangxi Province, is prone to LND due to its geographical location. Rainfall-induced LNDs account for over 70% of the county's LND. In this study, a digital modeling and machine learning approach is used to evaluate the susceptibility of rain-induced landslides in Xingguo County and generate a high-precision susceptibility map. Six influence factors are selected, and four machine learning algorithms, including support vector machine (SVM), decision tree (DT), back propagation neural network (BPNN), and random forests (RF), are used for susceptibility evaluation. A rainfall-induced landslide susceptibility map is derived, and landslide points are classified into five susceptive types. The experimental results show that the BPNN model achieved the best performance. The accuracy of the models is validated using the area under the receiver operating characteristic curve (ROC), area under the curve (AUC), accuracy (ACC), and kappa coefficient. The results showed that all models performed well, but the BPNN model achieved the best performance with an AUC of 0.75, ACC of 0.67, and kappa coefficient of 0.75.

Nowadays, Chinese power grid has developed very rapidly, and the transmission lines are massive. Our paper describes the use of an adaptive dynamic threshold algorithm and machine learning methods to detect wildfires in Yunnan province using Himawari-8. The algorithm extracts relevant features from the original NetCDF images and uses a dynamic threshold to identify wildfire pixels based on solar zenith angle and the proportion of cloud and non-vegetation pixels. Machine learning classifiers, including FCM+ SMOTE+SVM, are trained on the data using techniques to balance the dataset due to data imbalance. The improved classifier performs the best with a high accuracy for fire and non-fire pixels, outperforming other approaches including adaptive dynamic threshold, isolated forest, and one-class support vector machines. The FCM+SMOTE+SVM approach is shown to be robust for wildfire detection, but more data is needed to further improve its performance.

Landslide is one of the major geological disasters in China, which brings huge economic losses to our people every year. However, in the field of landslide treatment, the application of machine learning is scarce. In order to fill the gap in the field of landslide treatment measures based on machine learning. Firstly, random forest classification or regression algorithm was used to train and forecast each landslide treatment measure in this paper. Accuracy (ACC) was used to test the model accuracy of classification algorithm, and Mean Absolute Error (MAE) is used to test the model accuracy of regression algorithm. Random forest classification algorithm was adopted for non-numerical measures. And random forest regression algorithm was adopted for the numerical treatment measures. Secondly, the feature importance of the random forest model was calculated to obtain the more important features of each landslide treatment measure in this paper. Based on this, an optimized random forest model was constructed, and finally the optimal random forest regression and classification algorithm model suitable for landslide treatment measures recommendation was obtained. The training data dimensions of the model were reduced from 58 dimensions to 4-10 dimensions. The experimental results showed that our model could greatly improve the accuracy.

Wildfire is a serious natural disaster that poses a serious threat to the safety of human life and property. Currently, there are many researches related to satellite wildfire detection, but few can achieve near real-time monitoring results. Himawari-8 geostationary satellite can provide full disk data every 10 minutes, making near real-time monitoring of wildfires possible. In this paper, a wildfire detection method based on Himawari-8 for multi-temporal data is proposed. In our method, we use temporal convolutional network (TCN) to predict the brightness temperature and achieve excellent prediction results, the mean absolute error (MAE) is 0.28 K, mean square error (MSE) is 0.30 K2, and mean absolute percentage error (MAPE) is 0.10 %. Then, the predicted values combined with other features as model inputs, and machine learning classification models were used for wildfire detection. The experimental results showed that the combination of multi-layer perceptron (MLP) model and strategy 2 containing brightness temperature predicted values achieved an accuracy of 90.91% in wildfire detection.

The detection of electrical insulators in unmanned aerial vehicle (UAV) images using deep learning has made great progress in recent years, but little research has been conducted in the same field in remote sensing (RS) images. In this article, a novel method was proposed to detect insulators on 500-kV transmission towers in RS images. The proposed method consists of three components including 1) a super-resolution (SR) network to improve image resolution; 2) an object detection model to detect 110-, 220-, and 500-kV electrical power towers along transmission pipelines; and 3) a semantic segmentation network to identify insulators on the detected 500-kV towers. In addition, the online hard example mining (OHEM) method and class weight calculation method were utilized to handle the imbalanced data among different classes during training. The proposed model was evaluated on SuperView-1 and WorldView-3 satellite images collected in four regions. Experimental results show that the proposed method can effectively detect insulators in high-resolution satellite images and achieved the highest F1 score of 0.7952. The codes are available athttps://github.com/hardworking-jws/insulator-detection-remote-sensing

In recent years, the recognition accuracy of a semantic segmentation model on natural images can yield a very high level. Thus, it is of great significance to utilize semantic segmentation algorithm to obtain land use classification with remote sensing images. However, due to the large differences between natural images and remote sensing images, the standard semantic segmentation algorithm is not effective for land use classification of remote sensing images. In this article, the structure of high-resolution network (HR-Net) algorithm is improved according to the difference between the two kinds of images to make it more suitable for remote sensing images. Furthermore, in order to overcome the dependence of the semantic segmentation algorithm on a large number of high-quality prior data sets, some research experiments are conducted with the improved HR-Net domain adaptation model, and both of the adversarial domain adaptation model and the fusion domain adaptation model based on improved HR-Net and CycleGAN are designed to reduce the workload of manually labeling data. The extensive experimental results show that the classification of our improved HR-Net algorithm and the two domain adaptation models outperform other algorithms that demonstrates the effectiveness and superiority of our algorithms.

In this paper, an improved Douglas-Peuker (DP) algorithm is presented. In our method, the monotone chain is employed for simplification. And the$\varepsilon$-Voronoi diagram is used to eliminate the shortcomings of intersection and topological anomaly between monotone chains. Thus, the DP algorithm is simplified and improved. Our experimental results showed that our presented method is robust for the progressive transmission of vector map data.

At present, the urban functional system (UFS) has been changing with the rapid development of urbanization, which has brought serious challenges for the decision-making of urban planners. It is of great significance to investigate the robustness evaluation of urban functional system. Firstly, the composition map of urban functional area is obtained. Secondly, the center points of each functional area in the system are extracted to represent each functional area and to establish the coupling relationship between different functional areas. Thirdly, the robustness indices for different functional systems are designed. A series of simulation experiments about attack and damage are designed, and the change trend of robustness indices of urban functional system is investigated. Finally, the evaluation indices are established and a variety of simulation experiments are carried out to analyze the change trend of robustness indices. Our experimental results showed that the local attack in batches mode was the most serious mode to destroy the urban functional system. Therefore, more attention should be paid to avoid or alleviate the influence of the local attack in batches.

In the presented article, deep learning (DL) is employed on Sentinel-2 and Landsat imagery to assess and delineate the wildfire-affected burnt area. The most important aspect is the use of densely layered capsule architecture as an alternative to traditional convolutional neural networks (CNN) and max pooling. The presented investigation is a practical capsule-based wildfire detection and burnt area segmentation. Moreover, the research work presents a comprehensive assessment of capsule-based segmentation architecture. Due to the limitation of freely available open access annotated data, a dataset is manually constructed from scratch. The proposed algorithm provides prognostic delineation of the burnt area with an overall accuracy of 96%. The quantitative measures demonstrate that the proposed capsule-based architecture outperforms other state-of-the-art segmentation models (U-Net, DeepLabV3+). The capsule-based architecture shows good promise and can be scaled to other similar topographic regions.

In recent years, the number of geological disasters in Sichuan province has significantly increased due to the influence of earthquakes and extreme climate, as well as the disturbance to the geological environment by human activities. In the paper, the interferometric synthetic aperture radar (InSAR) technology was introduced to monitor potential geological hazards in different elevation intervals, taking parts of Dujiangyan City, Wenchuan County, and Mao County in Sichuan Province, China as examples. Firstly, the data such as synthetic aperture radar (SAR) images and precision orbit determination (POD) precise orbit ephemerides from 2018 to 2020, high-resolution optical satellite images and digital elevation model (DEM) were collected. Secondly, the differential InSAR (D-InSAR), persistent scatterer InSAR (PS-InSAR), small baseline subset InSAR (SBAS-InSAR), offset-tracking, and distributed scatterer InSAR (DS-InSAR) algorithms were used to invert the surface deformation of the study area. Finally, the remote sensing interpretation methods were comprehensively combined with the InSAR deformation anomaly monitoring results to monitor the potential geological hazards.

Study of the land use and land cover change near Qinghai Lake can help us recognize the characteristics of human activities and analysis the evolution law and formative reasons of regional ecological environment in this place. In this paper, we used the established database of land use/land cover change based on three phases remote sensing image data from 2005 to 2014 to analyze the change rate of land use and the structure of land use by using geographic information system (GIS) spatial analysis technology. We studied the characteristics of land use change and the regular of spatial distribution of land in the region of Qinghai Lake for nearly a decade. And the results of were listed as follows: From 2005 to 2014, farmland decreased by 0.96%; Woodland decreased by 0.14%; The grass decreased by 1.94%; Construction land increased by 0.27%; Waters decreased by 1.31%; Unused land increased by 4.06%.

Satellite detection has been an advantageous method in wildfire detection for its near-real-time monitoring and large coverage. In this paper, a time scale base wildfire detection is proposed based on himawari-8 satellite data. Firstly, we used adjusted Otsu algorithm to remove the cloud mask. Afterwards, 7 previous data of each 10 minutes before the current time of a pixel is input into a Long Short Term (LSTM) network to predict current data. Finally, the predicted data was compared with the ground truth to determine fire. The result showed a high accuracy in prediction and better performance in determining wildfire compared with traditional dynamic threshold method. The experiment is limited by the time of the selected wildfire data and expected to test the performance over different period.

The potential safety hazards for the power grid caused by explosion of insulators occur again and again. Thus, the detecting and monitoring of insulators on the transmission towers is vital. In the paper, a novel method was proposed to detect insulators with high resolution satellites images. First, the SuperView-1 (0.5 m) and WorldView-3 (0.3 m) scenes of Yunnan were gathered, and then gram-schmidt method was used to fusion the original images. Second, a wide deep super resolution network (WDSR) is used to enhance the images resolution by 4 times. Third, fake color output and 1% linear stretched were applied to enhance image detail. Then, an object detection neural network based on feature pyramid networks (FPN) was used to detect transmission tower. Finally, a high-resolution network (HR-Net) was used to detect insulators on the tower. For comparison, three different class weight calculation methods and online hard example mining (OHEM) training methods of HR-Net were also proposed. HR-Net-c2-ohem final achieved highest 0.8001 of F1-Score. Therefore, our proposed method is robust to detect the insulators of transmission line tower with high resolution satellites images.

Remote sensing integrated with deep learning (DL) improves wildfire assessment. The research has been done to scrutinize the areas affected by disastrous wildfires in Yunnan using DL. Wildfire identification and demarcation of the affected area have been limited to primitive thresholding and outdated machine learning classification techniques. Therefore, the research work incorporated DL in the wildfire scrutiny, and several of the most important considerations are investigated. The proposed research objective is to exploit the recent advancement of capsule-based DL together with the wildfire domain. The proposed dense structure provides highly efficient detection and segmentation of the burned area (BA). The BA dense capsule network (BA_EnCaps) is employed to extract and localize the burned zone with an overall accuracy of 98%. The model is evaluated quantitatively using accuracy, binary_cross-entropy, dice_loss, and mean square error (mse). The research aims to utilize the segmentation model to estimate the BA with great results. BA-EnCaps shows excellent accuracy in discriminating the spectral indices for the burned zone. The proposed method surpasses other segmentation benchmark techniques (U-Net, U-Net3p, SegCaps, Deep U-Net, and U-Net+) by substantially lessening the computing power. Finally, BA_EnCaps is compared with standard segmentation techniques and shows that DL-based models can assess wildfire better than conventional algorithms.

Landslides are common natural disasters that often cause serious impact and damage to human society. Since landslide disasters threaten people's production and life all the time, it is particularly important to predict the risk of landslides and to control landslide disasters. When studying landslide risk and deciding whether to treat the landslide, it is meaningful to classify and compare the risk of landslides so as to select those landslides with a higher degree of danger for priority treatment. The target of this paper is to extract factors related to landslide risk, and train a classification models for landslide risk. It employs ensemble machine learning algorithms to classify landslide hazards. Because the landslide feature has a large number of dimensions, this paper uses the PCA method to reduce the dimension. Due to the imbalance of the samples, this paper uses the SMOTE method to handle the imbalanced learning. The results of study show that the selected factors are highly related to landslide risk, the classification model in this paper has good accuracy.

With the development of urbanization in China, the land economy accounts for more and more percentage in the gross domestic product (GDP) of China. Therefore, the accurate assessment of urban residential land price is of great interest for the governments. In the paper, we introduced the transfer learning to assess the urban residential land price, taking the Shenzhen city in China as a case. First, we collected housing price, land price data and points of interest (POI) data. The POI data was quantified as the influencing factors of two price data. Second, we used housing price data and influencing factors to train a high-accuracy model based on deep belief network (DBN). Third, we kept the DBN model unchanged, and then used three new models connected in order to better assess land price. The three models are back propagation (BP) neural network, support vector machine (SVM), and random forest (RF). Finally, we used land price data and influencing factors with three models to find the best one. The models were trained by the method of five-fold cross validation. Our results showed that all of three transfer learning models had good accuracy but the model with random forest was more suitable for the urban residential land price assessment. Therefore, our proposed method is an efficient approach to assess the urban residential land price with transfer learning.

With the vigorous advocacy of the concept of green development, the protection and management of natural resources become more and more important. It is of great significance to study the classification of surface natural resources by remote sensing. In this paper, a high-resolution net (HR-Net) model is used to classify surface natural resources by Gaofen-1 (GF-1) satellite images and digital elevation model (DEM) data. First of all, we obtained the GF-1 satellite images, DEM data and census data of geographical conditions of the study area. And the first two kinds of data are integrated into five channels, which are red (R), green (G), blue (B), near infrared (NIR) and elevation channels. Second, we chose an area to make the labeled image with several classes contain surface natural resources. Third, we cut the image into training images and testing images, and the training images were made into 5000 images, 128 × 128 pixels to train the HR-Net model. Also for comparison, the experiment was carried out used the image without DEM data. Finally, we compared the accuracy, and our results showed that HR-Net model is useful and the image with DEM data has the better accuracy. Therefore, HR-Net and DEM data can be applied in practice to support the classification of surface natural resources.

In the presented article, machine learning (ML) is employed on advanced Himawari imager (AHI) to examine real-time fire and map damaged zone over Yunnan, China. The main emphasis lies in employing machine learning as an alternative to primeval thresholding, extricating, and scrutinizing thermal anomaly using infrared (IR). Firstly, Himawari brightness temperature (BT), band ratio, albedo, and BT differences are utilized to scrutinize fire break out. Then, to ensure pixels are clear and free from clouds, the Himawari cloud product is implemented. Finally, machine learning models such as random forest (RF), artificial neural network (ANN), and time-series long short-term memory (LSTM), a deep learning model, are used to precisely classify the active fire pixels and achieved accuracies of 0.96%, 0.95%, 0.92% respectively. The results evaluated using another AHI wildfire product and inter-compared with multi-source fire products.

In recent years, the problem of land subsidence in urban areas has attracted more attention. differential interferometric synthetic aperture radar (D-InSAR) is a common surface deformation measurement technology. About our research, first of all, the processing effects of the ascending and descending images, VV polarization and VH polarization of Sentinel-1A data in the study area are compared. The ascending image with better coverage in the study area and VV polarization with better interference processing effect are selected. Second, the filtering algorithm and unwrapping algorithm in D-InSAR are contrasted. In terms of filtering algorithms, the improved Goldstein method with the coherence coefficient to adjust the power exponent of the weighting function in the frequency domain had the best filtering effect. In terms of unwrapping algorithms, there are fewer unwrapping islands in the minimum cost network flow method. Finally, D-InSAR is used to process the Sentinel-1A data to obtain the surface deformation results of downtown Chengdu. Therefore, the deformation of downtown Chengdu based on Sentinel-1A data is abtained.

In the power system, there are a large number of capacitive devices, accounting for 40% to 50% of the total substation equipment. The healthy operation of capacitive devices is vital to the power system. With the vigorous advancement of power grid information construction, various types of power data have exploded, which has provided strong data support for the healthy operation of capacitive equipment. There is little research on the relationship between the defects of capacitive equipment and topography at home and abroad, such as altitude, slope, and aspect. This project conducts research on the relationship between defects of capacitive equipment and topography, and provides technical support and assistance for decision-making of power system construction. Firstly, 60 TIFF images with a resolution of 30m were obtained from the website, and 60 images were stitched using ArcGIS software, and then the slope and aspect data of the target location were extracted based on the latitude and longitude information. Secondly, we perform data cleaning and code on the original data. Thirdly, the relationship between the number of occurrences of equipment defects and altitude, slope, and aspect is programmed. The correlation is roughly deduced by calculating the number of occurrences of equipment defects and the parameters such as the covariance, correlation coefficient.

In addition to GPS and leveling, there are also synthetic aperture radar (SAR) measurements in the field of remote sensing. Differential interferometric SAR (D-InSAR) is a surface deformation measurement technology developed from synthetic aperture radar interferometry (InSAR). In the research of D-InSAR, although the processing flow is certain, the selection of data and process method is not universal. In the process of InSAR interferometric data processing, phase unwrapping is the key link, which directly affects the accuracy of digital elevation model (DEM). In this paper, the phase unwrapping methods of dual pass D-InSAR are compared. There will be islands in the process of unwrapping, and the less the islanding, the better. Through the situation of islanding, the unwrapping effect of region growth method and minimum network cost flow method is compared. The results show that the best method is the minimum cost network flow method.

Wildfires may cause great casualties and heavy wildfires are becoming more and more frequently all over the world in recent years. However, due to the environmental limitation, high manual-dependent operation is often impractical with other limits. In this paper, a transfer learning neural network based on long short term memory (LSTM) was used to detect wildfire based on Himawari-8. The real time dynamic threshold value detection for cloud mask based on the modified Otsu algorithm was used to fast and accurately remove cloud areas where wildfire detection is failed due to signal blocking. Then, the experiments were conducted with LSTM and other models. The experimental results showed that our method was positive for wildfire detection.

Due to an increasing demand of animal husbandry in arid (semiarid) area in China, grassland monitoring based on big data has become a proliferating research focus in recent years. However, the spectral of grass is interfered by topographic relief or that of forest in remote sensing image classification, leading to confusing pixels. In this letter, Tangbula grassland in the middle section of the Tianshan Mountains in Xinjiang (China) was selected as the research area. Upon analysis, we developed a novel composite multifeature deep learning method of capsule network to realize rapid and high-precision remote sensing recognition of the mountainous grassland, through combining the spectral bands with all the extracted features [normalized difference vegetation index (NDVI), topographic, and texture]. Using the new method, the accuracy of the grassland and overall classifications reached the highest values of 91.60% and 96.13%, respectively, greater than those of 85.58% and 92.18%, respectively, of normal classifications without input from texture and topographic features. Compared with the other methods, the method we applied is better than support vector machine (SVM), random forest, and artificial neural network in terms of grassland extraction and classification accuracy.

Impervious surfaces have become the most intuitive indicator in the process of urbanization. Timely and accurate information on impervious surfaces from remote sensing images is essential. It not only helps us understand the process of land use/cover change, but also the influences on human society and the environment. In this study, convolutional neural network (CNN) was used to extract the impervious surface in Chengdu city, Sichuan province, China. The overall accuracy in 2009 and 2017 were 98.75% and 99.76% respectively. From the results for 2009 and 2017, the impervious surface increased by 51.24 km2, Growth rate is 13.8%. During the process of urban expansion, suburban farmland was replaced by impervious surfaces and the area of impervious surface gradually increased.

Land use and cover change (LUCC) is a central component in current strategies for managing natural resources and monitoring environmental change. In this paper, we used maximum likelihood classification algorithm to obtain the supervised land use and cover classification. Four major land use and cover classes are identified and mapped from 2000 to 2015. The changes of land use and cover using Landsat images of the study area were analyzed. The results showed that: From 2005 to 2015, closed forest has increased and the annual rate of change was (+)3.3%. Open forest has an annual rate of change of (+)1.21%. Water bodies had an annual rate of change of (+)0.81%. While the settlements and bare lands had a decrease of 52.93 km2and the annual rate of change was (-)5.3%.

With the rapid development of urbanization, more and more attention has been paid to the structure of urban function zone. Thus, it is of great significance to investigate urban function zone. In this paper, we introduced the deep neural network (DNN) to infer the urban function zone with a supervised classification approach, taking the Shenzhen city in China as a case. First of all, the urban road networks of Shenzhen city were gathered and selected appropriately. Then, the fifth level road networks were utilized to segment the study region. Second, the communication data of different times and points of interest (POI) were collected. Then, the fifteen factors influencing urban function zone were derived. In addition, the urban function zone was divided into five types and the labeled examples with fifteen influencing factors were chosen. Third, the labeled examples were employed to train the DNN with different hidden layers compared with random forest (RF) and support vector machine (SVM). The models were trained with the approach of five-fold cross validation, and the average training accuracy with five times is taken as the accuracy of models. Finally, this paper compared the accuracy. It's been shown in the results that DNN was the optimum model and achieved the highest accuracy. Therefore, our proposed method is an efficient approach to infer the urban function zone.

Synthetic aperture radar (SAR) allows all-weather, day and night surveillance. Thus, it is of great significance for the ship detection and recognition. Because of the SAR special imaging mechanism, it is very difficult to extract the ship features with SAR image for the traditional target detection algorithm. In this paper, we proposed a approach which is composed of you only look once (YOLO) algorithm, sliding window detection strategy, and clustering algorithm. Firstly, the SAR images of GaoFen-3 and training dataset are gathered. Secondly, the experiments about the size of ship detection frame is carried out to find the optimum size of the frame for the training model. Thirdly, the ships are detected initially with YOLO v3 and fast region-based convolutional neural network (Fast-RCNN). Finally, the detected ships are clustered adaptively, and the experimental results of YOLO v3 and Fast-RCNN are compared and discussed at length. Our experimental results demonstrated that our method outperformed Fast-RCNN to detect the ships in the surface sea with low-resolution wide -band SAR images. Therefore, our approach is a robust method to detect the ships in the surface sea with SAR images.

Landslide disasters have caused incalculable losses to human. In China, 90% occurrence of landslides are directly induced by rainfall or indirectly related to rainfall. Because of its geographical location and the climate that belongs to the subtropical monsoon humid climate, the proportion of rainfall-induced landslides accounts for more than 70% of the total geological disasters in Bazhou district. In this paper, based on the geographic information system (GIS) technology, combined with the historical landslide hazard data, we conducted a study on landslide probabilistic quantitative model composed of landslide susceptibility evaluation and the rainfall intensity-duration threshold model. The research results showed that the prediction accuracy of rainfall model is 81.82%. And the landslide hazard prediction was carried out for rainfall-induced landslides and potential landslides, with a prediction accuracy of 90.91%. The research results showed that the meteorological early warning model results were consistent with the actual inspection results.

Drought is one of the main natural hazards affecting the environment and economy of countries all over the world. Fusing weather data with satellite images therefore becomes a superior method of identifying and monitoring drought in a given region. We established the relationship between land surface temperature (LST), the normalized differential vegetation index (NDVI) and rainfall data to derive areas of drought. Then, we obtained the indexes from the rainfall anomaly and NDVI anomaly as indicators which confirm the drought indicative claims of the maps produced. Our further examination of the NDVI, LST and rainfall maps indicate that the western, central and Volta Regions of the study area are the least prone to drought, with Axim (one of the most southern towns) in Ghana recording the highest rainfall in the country each year.

Global warming results in the rapid melting of glacial lakes in the Himalayas. In this paper, we took the Karakoram range in the Himalayas as the study area, and we derived the glacial lake area in 2001, 2014, and 2017 with semantic segmentation algorithm. Firstly, five high-resolution images in Mount Lucania were collected from Google Earth. Secondly, each image was labelled, and 80000 training images with the size of 256×256 pixels were derived using augmentation approach. Thirdly, a model of the stacking network of U-Net and SegNet based on these training images to extract the glacial lake was trained. Then, another five images of Karakoram range were derived from Google Earth as a testing dataset to demonstrate the performance of training model. Finally, the glacial lakes were extracted from the images of Karakoram range in the same month in 2001, 2014 and 2017. Our result showed that the area of the glacial lake in Karakoram range increased rapidly from 2001 to 2014, but decreased from 2014 to 2017.

Haze usually blurs the characteristics of images shotted in adverse weather conditions. It brings many challenges for computer vision such as object detection. Due to the lack of effective training dataset of remote sensing image, the dehazing usually utilize the physical model rather than the deep learning approach based on a large number of training dataset. An effective method to create training dataset may provide some new ideas for remote sensing image dehazing. In this paper, a novel approach based on the visual features rather than the physical counterparts is developed to create a training dataset for remote sensing images dehazing. Firstly, 400 haze images and 400 clear images with size of 240×240 pixels were gathered from Landsat 8. Secondly, the dataset was utilized to train the cycle-consistent generative adversarial network (CycleGAN) to derive an image transform model which can convert a clear image into a haze one. Thirdly, 4000 clear images with size of 240×240 pixels were collected from Landsat 8 and the images were transformed into the haze images with our model. Finally, the ratio of training dataset and testing dataset is set as 4:1. The haze images created by us were selected as the input and the original clear images were chosen as the output to train the convolutional neural networks to derive the dehazing models. The created dataset and the dehazing models derived were tested, and the experimental results showed that our approach is better to keep the brightnessinformation of the original image, but it is not good at keeping the chroma information.

The land resource is becoming scarcer and scarcer for a rapidly developing city. Thus, the land price assessment is important for the government to auction the land appropriately. In the paper, we introduced the deep neural network to evaluate the land price, taking the Shenzhen city in China as a case. Firstly, twenty influencing factors and land price data were gathered. Then, Shenzhen city was segmented into many grids with a size of 300 × 300 m. Secondly, the land price of each grid was derived with Kriging approach based upon the samples of land price. And the twenty influencing factors was quantified. Thirdly, the land price data and influencing factors were partitioned into training and testing datasets with the ratio of 8:1, and the training data were utilized to train the deep neural network based on regression analysis and classification with different hidden layers. Finally, the results were analyzed, and the deep neural network with the highest accuracy was selected as the optimum model. Therefore, our proposed method is an efficient approach to evaluate the land price with deep neural network.

A growing number of GWSs over networks have emerged. However, with the increasing implementation of GWSs, the difficulty of service discovery increases as well. This paper focuses on providing highly accurate and efficient GWS discovery by incorporating several techniques such as information retrieval, semantic matching, and quality of service (QoS) constraints. Experimental results demonstrate that the proposed GWS search method not only can provide accurate search results, but also provides an enhanced user experience.

Hyperspectral image is usually composed of hundreds of bands rich of spatial and spectral information. And this is an advantage for the common remotely sensed data. Thus, the classification of hyperspectral image could be of great value. However, the dimensionality of hyperspectral image may lead to the curse of dimensionality phenomenon when it is directly used for land use classification or other applications, making it difficult to be utilized effectively. In this paper, we presented a novel classification framework with capsule network based on the spectral and spatial information of hyperspectral images. At first, we use principal components analysis (PCA) to reduce the dimensionalities of hyperspectral image. Then, we use the capsule network to classify hyperspectral image. Our experimental result showed the novel classification framework is more efficient than other six popular methods. Therefore, the capsule network method is robust for hyperspectral image classification.

In recent years, the big data industry chain has become more mature. Analyzing and managing cities by utilizing various big data in cities has become a hot research topic. Urban functional regions discovering is one of the important applications. The mainstream in urban functional regions discovering are probabilistic topic models, such as latent Dirichlet allocation (LDA) based topic model, which seeing the regions as documents and their functions are their topics. These methods require feature engineering by hand, which will construct features of limited expressiveness. To overcome these methods' shortcomings, we introduced a deep learning topic model called document neural autoregressive distribution estimation (DocNADE) into urban functional regions mining. And we did an experiment to test its effect. The experimental result shows that this DocNADE framework has achieved a considerable result in urban function inference compared with Dirichlet Multinomial Regression (DMR) based topic model which is a state of the art of urban functional regions discovering.

In recent years, with the development of mobile location services and cloud computing technology, the collection and processing of mobile location information has become a reality. The mass database which is composed of mobile location data has promoted the development of the research on mobile location data. It is very important for us to understand the spatial distribution and temporal characteristics of moving patterns and identify the mechanism of motion formation, predict the future development of sports through trajectory clustering analysis. At present, trajectory clustering research mainly focuses on the spatial position changes of moving objects. Temporal constraints in spatial and temporal clustering are generally auxiliary information, but do not really participate in clustering. In this paper, a clustering algorithm for trajectory data based on spatiotemporal pattern is proposed. First, the curve edge detection method is used to extract the trajectory feature points. Then the trajectory is divided into sub track segments according to the trajectory feature points. Finally, the density based clustering algorithm is applied to cluster according to the temporal and spatial similarity between sub trajectories. The Hot spot analysis experimental based on Chengdu taxi GPS track data results show that the similarity measurement based on spatiotemporal features can get better clustering results because of both the spatial and temporal characteristics of the trajectory.

Development of a city cultivates regions with different functions such as working areas and entertainment venues. People in a city usually travel among these regions in certain movement patterns. Identifying those regions will facilitate government management and promote further development of the city. In this paper, we proposed a framework to identify urban functional regions in Chengdu city based upon mobility pattern and point of interest (POIs) information extracted from mobile check-ins data. Firstly, unlike GPS trajectories, location check-ins were discontinuous. Thus, the typical mobility patterns of location check-ins was mined. Secondly, an arrival/departure matrix based on the typical mobility patterns was constructed to obtain the topics of regions by clustering POIs. Because we considered a region's function as our topics, we transferred the problem into a topic modeling problem, and applied an improved probabilistic topic model to infer functions of the regions. We evaluated our approach with 227,428 check-ins in Chengdu collected from Sina Weibo from April 12 2012 to February 16 2013. The results showed that our method outperformed baseline methods solely clustering POIs.

In order to solve the problem of risk assessment for mountainous geological disaster in southwest of China, we selected Wenchuan county as the study area, where the geological disasters happen frequently. the digital elevation model (DEM), and other geographic data of Wenchuan county were utilized to evaluate the risk of geological disasters. Firstly, the weights of factors for geological hazard susceptibility were identified using analytic hierarchy process (AHP). Secondly, the fuzzy distinguish matrix based on the strength of membership function was established by combining AHP with fuzzy comprehensive evaluation (FCE). Thirdly, the geological hazard risk system was constructed according to the elevation, slope, distance from the river or the fault zone. Finally, we divided the study area into three risk types: high, moderate, and low. Our research results showed that the landslide numbers of high, moderate, and low risk are 11759, 16889, and 6075, respectively, and the corresponding percentages of area in Wenchuan county are 34%, 49%, 17%, respectively. Our results were in line with the historical disaster data. Therefore, the governments should pay more attentions to the geological disasters of these towns.

In the field of remote sensing, data acquired from a single sensor usually can't meet the needs of some special applications, because the information extracted from the data are often incomplete and limited. Data fusing can solve this problem, but it will lead to the redundant information. In this paper, we proposed a novel manifold learning approach to perform dimensionality reduction for the fusing optical and SAR data. And three typical manifold learning models, namely, ISOMAP, local linear embedding (LLE) and principle component analysis (PCA), were utilized to test the robustness of our method by comparing with the land cover classification results. Our experimental results showed that our proposed method obtained the best land cover classification results among these approaches for the fusing optical and SAR data.

Incorporating optical and SAR data to estimate impervious surface is useful but challenging due to their different geometric imaging mechanism. The recent development of deep convolutional networks (DCN) opens a promising opportunity. In this study, the typical DCN, AlexNet, was modified to estimate the impervious surface from optical and SAR data. GoogLeNet and the Support Vector Machine (SVM) were employed for comparison. Experimental results indicated the effectiveness of AlexNet with an accuracy of over 99%, outperforming both GoogLeNet and SVM. Furthermore, 60~80% of training samples outperformed the results from the whole training set under certain number of epochs, indicating that large number of training samples may not necessarily produce better results, depending on other factors (e.g. number of epochs). Generally, AlexNet was able to fuse the optical and SAR data and improved the accuracy of estimating impervious surface by about 2% compared with that using optical data alone.

Drought is a weather phenomenon widespread worldwide due to the water shortage or unbalance of supply and demand, and it's also one of the most serious natural disasters for human life and agricultural production. The middle reach of Yangtze river, one of China's most important grain producer, subjected to the sub-tropical monsoon climate, is prone to have droughts. This paper has practical implications as it build a model by depending on the normalized difference vegetation index (NDVI) and land surface temperature (LST) of moderate resolution imaging spectroradiometer (MODIS) between 2005 and 2009. Firstly, the 8-day LST and 16-day NDVI data, 8-day LST and 30-day NDVI data were utilized to construct the LST/NDVI feature space. Secondly, the temperature vegetation dryness index (TVDI) images of the reach were derived respectively. Thirdly, the temporal evolution and spatial variation of drought was analyzed. Finally, the results of two different years were compared to analyze the drought in the reach. Our study showed the drought in May was more severe than that in other months. Therefore, a severe drought event is more likely to happen in May in the middle reach of Yangtze river and more measures should be taken to alleviate the loss for the governments.

Hyperspectral image (HSI) is usually composed of hundreds of bands which contain very rich spatial and spectral information. However, the high-dimensional data may lead to the curse of dimensionality phenomenon when it is used for land use classification or other applications, making it difficult to be utilized effectively. In this paper, we developed a deep learning classification framework based on the spectral and spatial information of hyperspectral image. Firstly, the deep learning features in different layers could be extracted automatically. Secondly, based on the learned deep learning features, we could obtain the classification of hyperspectral image with logistic regression (LR) classifier. Finally, we compared our approach with other methods including quadratic discriminant analysis with the multilevel logistic spatial prior (QDAMLL), logistic discriminant analysis with the multilevel logistic spatial prior (logDAMLL), linear discriminant analysis with the multilevel logistic spatial prior (LDAMLL), subspace multiclass logistic regression with the multilevel logistic spatial prior (MLRsub MLL), support vector machine on extended morphological profiles (SVM/EMP), support vector machine on expectation maximization and post-regularization (SVM-EM-PR). The experimental results showed that our method obtained the optimum accuracy, which was better than the other six approaches. And the OA was up to 99.39%. Therefore, the deep convolutional neural networks (DCNNs) is a robust method for land use classification with hyperspectral image.

Land use and land cover change (LUCC) is necessary to explore the factors leading to heavy drought and rainy-flood disaster in some districts of Sichuan province. A method based RS, GIS, GPS and Google earth (GE) is presented to establish LUCC database in Sichuan province and Chengdu district. At first, LUCC is interpreted based on the new temporal images and the land use and land cover database from TM in 2000.Secondly, some ground objects, which could not be identified in the new temporal images, were interpreted utilizing GE with some higher spatial resolution images. Thirdly, the new interpreted LUCC was validated in the field with GPS handheld receiver. Then, LUCC of Sichuan province was updated. A comparative analysis of LUCC between in Sichuan province and in Chengdu district was conducted and the result showed: (1) a large amount of farmland in Sichuan Province was occupied from 2000 to 2005 and the area is 84 573 ha. While construction land gained obviously and the area was 35 828 ha. The dynamic degree of construction land was 111.100/00from 2000 to 2005. The LUCC demonstrated that the economy of Sichuan province continued to develop, the cities were overspreading and the urban heat island effect was deteriorated from 2000 to 2005. (2) A large amount of farmland was also occupied in Chengdu district from 2000 to 2005, the area amounted to 12 989 ha. The farmland lost was mainly changed to construction land, amounting to 93%. And the dynamic degree was 117.410/00from 2000 to 2005, which was bigger than that in Sichuan province.

Hyperspectral remote sensing image (HSI) consists of hundreds of bands that contain rich space, radiation and spectral information. The high-dimensional data can also lead to the curse of dimensionality problem making it difficult to be used effectively. In this paper, we proposed a manifold learning algorithm to reduce the dimensionality for HSI data. For high dimensional datasets with continuous variables, it is often the case that the data points are arranged along with low dimensional structures, named manifolds, in the high dimensional space. Manifold learning aims to identifying those special low dimensional structures for subsequent usage such as classification or regression. However, many manifold learning algorithms perform an eigenvector analysis on a data similarity matrix whose size is N×N, where N is the number of data points. The memory complexity of the analysis is at least O(N2) that is not feasible for a regular computer to compute or storage for very large datasets. To solve this problem, we used statistical sampling methods to sample a subset of data points as landmarks. A skeleton of the manifold was then identified based on the landmarks. The remaining data points were then inserted into the skeleton by Locally Linear Embedding (LLE). We tested our algorithm on AVIRIS Salinas-A data set. The experimental results showed that the HSI dataset could be reduced to a lower-dimensional space for land use classification with good performance, and the main structure was preserved well.

High-dimensional data such as hyperspectral images contain abundant information of surface radiation. But the massive redundant information makes it complex to be utilized conveniently. To solve this problem, a manifold learning dimensionality reduction framework for hyperspectral image is proposed. Firstly, statistical sampling methods were used to sample a subset of data points as landmarks. A skeleton of the manifold was then identified basing on the landmarks. The remaining data points were then inserted into the skeleton by Locally Linear Embedding algorithm. At last, original data sets and data sets reduced with different manifold learning approaches were classified by KNN classifier to evaluate the performance of the proposed framework. The framework was tested on AVIRIS Salinas-A dataset. The experimental results showed that the tradeoff of accuracy with different landmarks is of great significant. Insufficient landmarks lead to low accuracy and excess landmarks may spend a considerable amount of time.

Under the indoor building environment, when the fires and other accidents occur, how to effectively organize the masses evacuation and fire rescue, is closely related to the safety of people's lives and property and has become a critical problem of public concern. This paper presents an improved ant colony algorithm (ACO) to solve the problem of how to optimize the evacuation route and rescue route when an accident occurs. According to the key factors affecting people emergency evacuation, such as indoor building environment, fire and its combustion products, problem of path's optimal selection, etc., we propose an emergency evacuation model, based on the model it can give an optimal evacuation route for the mass and an optimal rescue route for the firefighters. We also analyzes the search results, it shows that the search results is robust and reasonable.

In China, drought is one of the major environmental disasters, which bring great harm to the people. The middle reach of Yangtze River is the most important base to produce grains in China. Influenced by the summer monsoon, the drought occurs frequently. In our paper, the NDVI and LST from MODIS data were utilized to calculate the TVDI (Temperature Vegetation Dryness Index), which were used to monitor the drought of the study area. Meteorological drought indices were calculated from 10-day precipitation, temperature and evaporation data of 94 meteorological stations, including precipitation standardized variables, dryness and relative moisture index were used to analyze the degree of drought and the area of drought. The results showed that TVDI is significantly related to soil moisture.

Rocky desertification is a type of land desertification. It comes from the fragile ecological and geological environment, where the human activity is very strong and the land productivity is degraded severely. As a natural disaster, rocky desertification is very destructive, and it is very difficult to be recovered. The karst region of China in southwest is the world's concentrated karsts region. It is also one of the largest contiguous karsts regions. The karst region is also the most typical ecological fragile regions in China. We utilized the ETM+ images in 2000 to study the rocky desertification of the north regions in Guangxi province in the past ten years. Firstly, the rocky exponential model was established to extract rocky desertification information of the region. Then, the RGB image was composited to interpret and obtain the rocky desertification. Our experiment showed that rocky desertification of the karst region can be classified into no rocky desertification, moderate desertification, and severe rocky desertification.

It is a robust and efficient method that utilizes morphological operators, and Otsu threshold together with GIS road vector to detect vehicles from high resolution highway aerial images. Our previous experiments showed that the method resulted in a high correctness, completeness, and quality. However, we did not discuss the relation between the accuracy and the size of structure element for vehicle detection with high resolution highway aerial images. The space resolution of our aerial images is 0.15×0.15m, and most vehicles' size is normally 5×2m, approaching 34×14 pixels. Thus, we selected a disc as structure element, but the disc's radius should be less than 7 pixels. We compared the vehicle detection accuracy with 3 pixels, 4 pixels, 5 pixels, 6 pixels, and 7 pixels of disc with different size. Our result showed that the radius with 5 pixels or 6 pixels obtained the highest accuracy.

Vehicle detection from high resolution aerial images has been studied for many years. However, a robust and efficient vehicle detection is still challenging. In this paper, a novel and robust method for automatic vehicle detection from aerial images was presented. In this method, a GIS road vector map is used to constrain a vehicle detection system to parking lot networks, edge detection and morphological preprocessing method are used to identify candidate vehicle pixels. Different types of vehicle templates are selected to adaptively detect the similar vehicles by their correlation coefficient with the same size of the window. Experiment was conducted using 0.15 meter resolution aerial images, the result demonstrated that the new method had an excellent detection performance.

Vehicle detection from high resolution aerial images has been studied for many years. However, a robust and efficient vehicle detection method is still challenging. In this paper, a novel and robust method for automatic vehicle detection from highway aerial image was presented. In this method, a GIS road vector map is used to constrain a vehicle detection system to the highway networks. After the structure element is identified, morphological preprocessing method is used to identify candidate vehicles. Experiment is conducted with 0.15 m resolution aerial image. And the result demonstrated that the novel method has an excellent detection performance, thus the method is very promising.