Recently, urban heat islands (UHIs) have emerged as a significant challenge for humanity due to the effects of urbanization and fast industrial growth. The primary factors contributing to UHI involve the substantial heat emitted by urban structures and human-made heat sources. These heat sources lead to an elevation in the temperature of urban areas compared to their surroundings. Various research methodologies have been used to study and analyze this effect and to correlate it to climate change. It has been found that integrating green spaces within cities would positively impact urban heat islands by lowering their land surface temperature (LST). The main objective of this study is to investigate the spatial and temporal changes in Dubai’s LST over the last twenty three years and correlate them with Land Cover Land Use (LCLU) data. The study reveals an inverse urban heat island effect in the Dubai area, wherein the summer day temperature in urban areas is lower than the surrounding regions. Conversely, winter nights exhibit higher temperatures in urban areas. Furthermore, regions experiencing significant industrial growth were identified as statistically significant using the Mann-Kendall test.

A key aspect of a sustainable urban design is reducing expo-sure to air pollution by enhancing airflow and pollution dis-persal. Few research has been conducted to standardize the management of urban development procedures that account for air quality and human exposure to different gaseous pollutants. In this study, a time-space statistical hotspot analysis was carried out using high-resolution imagery of concentration for various air pollutants over the Emirate of Dubai. Spatial and temporal air quality patterns and their correlation with land-cover land-use (LCLU) were analyzed to map and quantify risks connected to air pollution and poor urban planning strategies. Afterward, using a Geographic Information System (GIS), a theoretical structure for urban management zones accounting for urban air circulation and less human exposure to air pollution was implemented, to identify areas with fewer ventilation processes. The methodology of this study can be implemented over different urban areas to identify poor air quality areas that require comprehensive investigation and to assist the development of urban planning strategies.

Accurate flood mapping plays a critical role in disaster management, allowing for effective response and mitigation efforts. Thus, researchers seek to boost the accuracy of flood mapping algorithms, especially in terms of generalization capability and minimizing False Positive and False Negative detection. This paper presents a robust flood mapping algorithm from SAR images via Deep Convolutional Neural Network (DCNN) that follows encoder-decoder scheme. By introducing Bidirectional Convolutional LSTM (ConvLSTM) layers into its architecture, the proposed Temporal-Spatial Encoder-Decoder Network (TSEDN) network is able to extract temporal information and produce more accurate change maps. The training and testing are carried using OMBRIA dataset, which is known to be challenging to train. The proposed network is evaluated and compared to other state-of-the-art approaches in terms of Overall Accuracy (OA), Precision, Recall, and mean Intersection over Union (mIoU).

This paper proposes a novel multi-branch feature fusion network for PolSAR image classification and interpretation. It is built using Complex-valued Convolutional Neural Networks (CV-CNNs). The proposed approach utilizes extraction of polarimetric features at each branch to achieve high classification accuracy. Moreover, Squeeze and Excitation (SE) is also introduced within the model’s architecture. SE block improves channel interdependencies with almost no additional computational cost. The proposed approach is tested and evaluated using Flevoland benchmark dataset. Experiments demonstrate the effectiveness of the proposed attention based shallow to deep CV-CNN model for PolSAR image classification in terms of Kappa Coefficient (k), Overall Accuracy (OA), and Average Accuracy (AA) metrics.

Mangroves are coastal ecosystems with enormous ecological benefits. These coastal protectors provide a living environment to many marine organisms, and it is considered a unique contributor against climate change in their carbon storage and sequestration process. Mangroves experience severe losses due to natural factors and intensive anthropogenic activities. Therefore, mapping, monitoring, and obtaining consistent recent information about these valuable resources is essential for conservation and protection. The United Arab Emirates (UAE) is the home of sixty million mangroves covering an area of more than 180 km2and storing 43,000 tons of carbon dioxide yearly [1]. The mangrove area located in the coastal region of UAE provides various benefits to the region and is considered a protective shield against the risk of erosion and sea intrusion. Therefore, UAE promised in the Conference of the Parties 2026 (COP26) to plant 100 million mangroves by the year 2030 [1]. Remote sensing and digital image processing techniques had proven to understand the mangrove ecosystem dynamics. Therefore, this study aims to investigate the changes in the Mangrove area and the effect of these changes on coastal erosion hazards over the last 20 years. The first step is to use a pixel-based machine learning (ML) classifiers along with multi-temporal, medium-resolution Landsat satellite images within Google Earth Engine (GEE) cloud computing platform, to create multi-temporal mangrove distribution maps of the UAE coastal area during the last 20 years. Second, qualitative and quantitative evaluations are conducted using ground truth data to validate the robustness of the proposed methodology and the accuracy of the results. Finally, coastline analysis is carried out using open-source tools, such as Digital Shoreline Analysis System (DSAS) [2] to estimate coastline changes and analyze coastal erosion risk. This method allows the identification of the mangrove gains and losses, as well as measurement of the change of coastline (accretion and erosion) over the past 20 years. The generated maps can lead to improvements in the ecosystems’ management and protection procedures. This study presents an effective workflow for mangrove detection and temporal mapping, using open-source medium-resolution satellite images, big data processing platforms, such as GEE, and open-source tools, such as DSAS.

The automated recognition of human emotions plays an important role in developing machines with emotional intelligence. Major research efforts are dedicated to the development of emotion recognition methods. However, most of the affective computing models are based on images, audio, videos and brain signals. Literature lacks works that focus on utilizing only peripheral signals for emotion recognition (ER), which can be ideally implemented in daily life settings. Therefore, this paper present a framework for ER on the arousal and valence space, based on using multi-modal peripheral signals. The data used in this work were collected during a debate between two people using wearable devices. The emotions of the participants were rated by multiple raters and converted into classes in correspondence to the arousal and valence space. The use of a dynamic threshold for ratings conversion was investigated. An ER model is proposed that uses a Long Short-Term Memory (LSTM)-based architecture for classification. The model uses heart rate (HR), temperature (T), and electrodermal activity (EDA) signals as its inputs with emotional cues. Additionally, a post-processing prediction mechanism is introduced to enhance the recognition performance. The model is implemented to study the use of individual and different combinations of the peripheral signals, as well as utilizing annotations from different ratings. Additionally, it is employed for classification of valence and arousal in an independent and combined fashion, under subject dependent and independent scenarios. The experimental results have justified the efficient performance of the proposed framework, achieving classification accuracy 96% and 93% for the independent and combined classification scenarios, accordingly. The comparison of the achieved performance against the baseline methods shows the superiority of the proposed framework and the ability to recognize arousal-valance levels with high accuracy from peripheral signals, in real-life scenarios.

Long-term, continuous physiological recordings are currently being intensely investigated for tracking emotions. Emotional valence has been of more interest due to its relevance to cardiac and neurophysiological disease. In this research, multiple configurable convolutional neural networks (CNNs) were developed for different image-encoding techniques used as their input. Ensemble classification was then used to achieve a combined performance of the multiple CNNs by training a simple support vector machine (SVM) classifier using the last output layers of the CNNs as its input. Valence-labelled signals from the heart rate (HR) recorded using a wearable sensor from a wristband in a daily setting for one week from 80 participants were used for the image transforms. Accuracies of more than 91% were achieved with the classification ensembling, showing an improvement of the binary classification of emotional valence by more than 19% compared to using CNNs on their own.

The paper proposes mid-level features for socio-cognitive classification of crowd behavior in public spaces, particularly in the context of monitoring social interactions during, e.g., pandemic restrictions. The classification method follows a recently proposed categorization [37]. The features are built using statistics obtained from detection and tracking results forc crowd components, i.e. individuals and their groups (any typical detectors and trackers can be used). The features are defined by static (if obtained from the current frame) or dynamic (if derived from consecutive frames) parameters characterizing the crowd. Subsequently, the features extracted from a number of most recent frames are fed into a fully-connected shallow neural network to identify the type of social interactions in the monitored space. The experimental feasibility study shows encouraging performances of the approach. In particular, the results are far more discriminative than in the other solution (which, at the moment, is the only publicly known benchmark).

A CNN-based framework is presented for extracting and classifying from static images of crowd (acquired from surveillance systems) individuals, small groups and large groups. A novel approach to the network training has been investigated. Instead of manually outlined ground-truth data, we use automatic annotations by alternative baseline algorithms (which consider both motion and appearance). The proposed CNN detectors are initially trained over rather limited amounts of data. Nevertheless, the detectors are subsequently updated (fine-tuned) by using new batches of automatically annotated samples. Those test samples are periodically acquired by the baseline algorithms from the future surveillance data. Fine-tuning is performed when noticeable differences appear between results by the CNN-detectors and the results of baseline algorithms (which may indicate changes in visual conditions, scenarios or updates in the baseline algorithms). We preliminarily demonstrate that satisfactory performances of CNN-based detectors can be achieved, even if the baseline algorithms have limited accuracy. Actually, it was noticed that fine-tuned CNN-detectors can be superior to the baseline algorithms used for automatic annotation of training data (even though the baseline algorithms process both static images and video-sequences). Since only static images are used once the detectors are fully trained, the presented solution can simplify complexity of systems automatically evaluating structure and behavior of crowds.

In this paper, a temporally iterative Gaussian Mixture Model (GMM) of Dynamic Texture (DT) for target detection using a moving PTZ camera, is proposed. Camera movement in a PTZ sensor causes motion-based target detection techniques to fail for the periods affected by the scene change. This is because the whole scene is considered a representation of the target motion. When the camera is in motion, conventional background models remain invalid until the time that the model has adapted and updated its parameters to the newly perceived scene. The proposed model is based on an iterative modeling of spatio-temporal patches that represent the visual scene using GMM-of-DT. During the initial iteration of the proposed GMM-of-DT model, the input video is temporally segmented into clips in a manner that separates global from local motion. Further, parameters of the GMM-of-DT model are estimated for each temporal segment and in subsequent iterations updated adaptively to generate the final foreground mask. The proposed technique is tested and verified on video scenes from public datasets.

In this paper, a motion and appearance saliency combined detection framework for hierarchical representation of targets from groups to individuals in crowded scenes of surveillance videos is proposed. Big data analytic solutions within surveillance often require compact representations for target (s)- of-interest that allows simultaneous micro (individualistic) and macro (holistic) levels of inference on visual information. The target detection method proposed in this paper combines the estimation of motion saliency through dynamic texture (DT) based Gaussian Mixture Model (GMM) and appearance saliency through person detection using combined Histogram of Oriented Gradient (HOG) and Local Binary Patterns (LBP) feature sets. The saliency models are tightly integrated such that initially motion information is used to update and improve detection within an appearance framework, which in turn compliments the motion segmentation for accurate localization of people in groups. The improved people detection thus proposed is capable of eliminating false detections and can accurately delineate individuals within groups. The quantitative and qualitative results of experiments conducted on benchmark datasets have proven the validity and robustness of the proposed technique.