Landform elements such as ridgeline and valleyline play a crucial role in understanding the Earth’s surface, its composition, geomorphic processes, and the changes over time. With the rapid advancement of remote sensing technologies, high density and very high-density point cloud data have enabled fine-scaled landform characterization and terrain analysis. However, extracting ridgeline and valleyline at fine scales remains a challenge for conventional approaches, which are often difficult to address inherent errors and uncertainties in the data, and distinguish different landform elements that exhibit similar characteristics. To address these challenges, we generate a large-scale dataset involving the labels for two major landform elements (ridge lines and valley lines) at different scales. We then propose a framework that combines spatial-contextual approach and neural networks for multiscale ridgeline and valleyline extraction. In the experimental section, we evaluated the performance of our proposed approach by comparing the accuracy and recall of ridgeline and valleyline extraction using a variety of machine learning-based and deep learning-base approaches. The results demonstrate that our method effectively leverages the advantages of feature learning and enhances the robustness of multiscale terrain morphology extraction and recognition. We hope our work can provide a explainable deep learning solution for multiscale landform element extraction in the community of geomorphology.

Mining cities are an essential part of China's urban agglomerations, and as mining cities continue to develop, ecological and environmental pollution has become a primary problem. In the present study, the Aerosol Optical Depth (AOD) retrieval of major mining urban agglomerations in China from the Gaofen-1 satellite data. Then a new hybrid model based on CTM (chemical transport model) Transport Model 5 (TM5) and GTWR (Geographic Time-Weighted Regression model) is proposed for PM2.5 and PM10mass concentration estimation. According to the different transformation stages and urban structure of mining cities, the temporal and spatial analysis of particulate matter characteristics is carried out in mining urban agglomerations. The estimated result for PM2.5 is verified at ground stations with R2 of 0.956 and RMSE (Root Mean Square Error) of 10.377 μg/m3, Moreover, the estimated result for PM10is verified at ground stations with R2 of 0.926 and RMSE of 16.669 μg/m3, The results indicate that PM2.5 and PM10have distinct spatial and temporal distribution patterns as Chinese mining cities are undergoing different types of transformation processes.

The advent of the era of Remote Sensing Big Data has produced a large number of processing and analysis tasks, which require powerful computing capabilities to support. The computational efficiency of distributed computer clusters which are the most commonly used parallel computing architecture for high performance computing can be significantly improved through an effective task scheduling strategy. In this paper, in order to improve data computing efficiency, we propose a dynamic load balancing strategy for remote sensing data processing workflow tasks based on the Hungarian algorithm for heterogeneous distributed computing clusters. We also compare this strategy with the classic load balancing algorithm. We find that the speed-up effect of the strategy proposed in this paper is better, and the speedups become more pronounced as the number of tasks increases.

The representation of landform element varies over multiple scales, or multiresolution digital elevation model (DEM) and its derivatives. When more details of land surfaces are available to be characterized based on the existing high spatial resolution elevation products, the influence of scale variation might become more significant. This poses a demand for determining a scale-independent approach being competent to support multiscale landform element detection on high-resolution DEMs. Although the practicability of the state-of-the-art scale-independent approaches have been reported on moderate-resolution DEMs, how these approaches perform based on the multiscale data including high and moderate spatial-resolution DEMs is still unexplored. This letter evaluates the performance of four scale-independent techniques including filtering, spatial pyramid, multiscale segmentation, and spatial-contextual approach in landform element detection on different spatial resolution DEMs. The experimental results show that spatial–contextual approach is more effective to support multiscale landform element detection than others.

In this paper, PM2.5 and PM10 in mainland China were estimated by using the Geographically and Temporally Weighted Regression model and FY-4 AOD data. Based on the GTWR model, the PM was estimated by BLH, RH, time, space and AOD. Taking June 2, 2019 as an example, the feasibility of FY-4 data in estimating PM2.5 and PM10 mass concentrations was analyzed and confirmed.

In this paper, through the analysis and comparison of three commonly used atmospheric environmental capacity estimation methods, we find that the existing methods have many limitations in the aspects of data base and the factors considered. Taking Xuzhou City in Jiangsu Province as an example, based on the air pollution multi-source model, environmental impact assessment and primary pollutant simulation are carried out by using remote sensing data, ground monitoring station data, pollution emission inventory data and meteorological data. In addition, the iterative algorithm of multi-pollutant environmental capacity with the joint constraint of$\text{PM}_{2.5}$and$\mathrm{O}_{3}$is established to recalculate the urban atmospheric environmental capacity.

Water quality, which is a key concern associated with large-scale canal operation and management, is vulnerable to the influences from short-term weather variations and artificial activities. Chlorophyll is one of the key indicators to measure the water quality and usability for drinking and irrigation in the canal system. However, previous research designed the state-of-the-art algorithms regarding water chlorophyll estimation using medium-resolution remote sensing data (e.g., Landsat), which has insufficient resolution to capture canals that are usually narrower than one pixel in such data. High-resolution imageries covering the whole canal network might include only either visible wavebands (i.e., red, green, blue bands) or cost thousands of dollars for an effective investigation on real-time water chlorophyll monitoring. Thus, the strategy designed for water chlorophyll analysis in a canal should consider an appropriate tradeoff among spatial resolution, the spectrum helpful for chlorophyll detection, and the financial burden. This letter presents our efforts on identifying and assessing the extent of the Planet data for measuring chlorophyll degree of canal waters. The experiments show that although Planet can represent the relative variation in water chlorophyll concentration, new algorithms are still necessary for accurate results regarding water chlorophyll variations in a canal system.

Maintenance planning of groundwater delivery infrastructure, such as canals, requires labor-intensive field inspection for properly allocating maintenance resources to sections of water infrastructure based on their deterioration conditions. Defective canal sections have cracks where the water delivery performance degrades. In practice, canals can be tens or even hundreds of miles long. Manual canal inspections could take weeks, while could hardly achieve comprehensive water leakage assessment. Another difficulty is that most cracks are developing under the water. Without drying up the canals, inspectors could not observe underwater conditions. They would have to assess visible parts of water facilities and environments (e.g., humidity changes and vegetation growths nearby) for prioritizing canal sections in terms of leaking risks. Even experienced inspectors need much time to complete a reliable canal condition assessment. This paper presents a deep-learning approach augmented by canal inspection knowledge to achieve automated and reliable water leak detection of canal sections from Landsat 8 satellite images. Such integration utilizes the domain knowledge of experienced inspectors in augmenting the deep-learning methods for more reliable image pattern classification that supports rapid canal condition assessment. Compared with machine learning algorithms trained by raw satellite images manually labeled as leaking, domain-knowledge-augmented deep learning algorithms use satellite image augmented by pixel-level land surface temperature (LST), fractional vegetation coverage (FVC) and Temperature Vegetation Dryness Index (TVDI) as training samples. Specifically, LST, FVC, and TVDI for each pixel are physical parameters derived from Landsat 8 satellite images by remote sensing methods. The “leaking” or “no-leaking” labels of the training samples are from the concrete surface inspection records collected during annual dry-ups of the canal from 2016 to 2019. Testing results on data sets collected for canals flowing through both urban and rural areas show that the proposed approach can achieve recall at 86%, precision at 86%, and accuracy at 85%. The precision, recall, and accuracy of the proposed approach are similar to a conventional deep learning algorithm that uses raw images for training while being more computationally efficient. The reason is that the new approach only processes three channels rather than the 11 channels in raw images. The authors also tested how different combinations of environmental features influence the performance of the algorithm. The results showed that two feature combinations: (LST, FVC) and (LST, FVC, TVDI) achieve the most robust performance in diverse geospatial environments.

This article introduces our research in developing a probabilistic model to extract linear terrain features from high resolution Digital Elevation Models (DEMs). The proposed model takes full advantage of spatio-contextual information to characterize terrain changes. It first derives a quantifiable measure of spatio-contextual patterns of linear terrain features, such as ridgelines, valley lines and crater boundaries, and then adopts multiple neighborhood analysis and a probability model to address data uncertainty in terrain surface modeling. Different from traditional approaches, the proposed model has the ability to achieve near-automated processing. It also supports effective extraction of terrain features in both smooth and rough surfaces. Through a series of experiments, we demonstrate that the proposed approach outperforms existing techniques, including thresholding, stream/drainage network analysis, visual descriptor detection, object-based image analysis and edge detection. This work contributes to both the geospatial data science and geomorphology communities with a new way of utilizing high-resolution imagery in terrain analysis.

Objects are always represented by different scales on remote sensing imageries, which poses challenges for the state-of-the-art convolutional neural networks for multi-scale object recognition. This paper proposes atrous region proposal to facilitating detect other objects within different scales in an ad-hoc manner.

The deep learning has been widely used in scene recognition of remote sensing images. However, the accuracy of deep learning relays on the size of training dataset to the utmost. The remote sensing images have various spatial scales and semantics, which are not fully considered in the existing datasets. In this paper, a benchmark remote sensing dataset named as Cross-Scale Remote Sensing dataset of Shenzhen Institutes of Advanced Technology (CSRS-SIAT) is proposed, which has about 100 classes according to the land cover and land use field, and due to the cross-scale characteristics of remote sensing images, the experiments using traditional and state-of-the-art deep learning algorithms shows that there still need more efforts to achieve better results.

By providing detailed height and intensity land surface information, high-resolution LiDAR data have proved to be effective in supporting land classification when combined with other major geospatial data sources, such as hyperspectral images. However, rectifying and fusing multisource geospatial data involves what normally is a manual and time-consuming process. In this letter, we propose a geographic object-based image analysis approach to enable semiautomatic land classification and mapping using LiDAR elevation and intensity data. The methodological framework consists of a series of operations, including preprocessing, object-based segmentation, creation of statistical variables from elevation and intensity, and semisupervised classification. We have successfully applied this approach to the classification of multiple land features, including asphalt, grass, barren land, swimming pool, shrubland, pavement, and buildings. Results show that our proposed approach performs better than LiDAR analysis methods in classifying different land parcels.

This letter presents a novel hierarchical semisupervised support vector machine (SVM) for classification of hyperspectral images. The method exploits the wealth of unlabeled samples by means of their cluster features. The method learns a suitable framework for classifying cluster features by a semisupervised SVM and thus makes use of advantages of clustering and classification. Experimental results demonstrate that the proposed classification method is effective for hyperspectral image classification when a few labeled samples are available. Another advantage of the proposed method is that the hierarchical structure can simultaneously take clustering and classification information into consideration.