Application of Satellite Remote Sensing in Geospatial Monitoring
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".
Deadline for manuscript submissions: 30 June 2024 | Viewed by 7995
Special Issue Editors
Interests: geoinformation; geographical analysis; spatial analysis; mapping digital mapping; satellite image analysis; geospatial science; spatial statistics satellite image processing; advanced machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing; photogrammetry; lidar; unmanned aerial vehicles; geodesy; geographic information system; geoinformation; satellite image analysis; mapping; 3D reconstruction
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
With the recent advances in remote sensing technologies for Earth observation (EO), many different remote sensors now collect data with distinctive properties. EO data have been employed to monitor croplands and forested areas, oceans and seas, urban settlements, mountainous areas, climate-related processes, and natural hazards. The spectral, spatial, and temporal resolutions of remote sensors (e.g., optical, radar) have been continuously improving, making geospatial monitoring more accurate and comprehensive than ever before. Therefore, newly developed deep learning methods and machine learning techniques are allowing us to tackle problems that were considerably difficult to approach just a few years ago.
Nevertheless, many challenges still remain in the remote sensing field, which encourages new efforts and developments in order to better understand remote sensing images via image-processing techniques. Therefore, this Special Issue aims to present new machine and deep learning techniques within new application areas in remote sensing acquired from unmanned aerial vehicles (UAVs), aircraft, satellite platforms and different sensors (multispectral/hyperspectral optical, radar, lidar). Review papers on this topic are also welcome.
Therefore, authors are encouraged to submit articles on topics including but not limited to the following:
- Deep learning methods using remote sensing data;
- Multitemporal and multi-sensor data fusion and classification;
- Time-series image analysis;
- Agricultural and forest monitoring;
- SAR-based features;
- Optical-based features;
- Land-use and land-cover change classification;
- Usage of the analysis-ready image collections and cloud computing services;
- Geospatial data analysis for change detection.
Dr. Dino Dobrinić
Dr. Mateo Gašparović
Guest Editors
Manuscript Submission Information
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Keywords
- deep learning methods using remote sensing data
- multitemporal and multi-sensor data fusion and classification
- time-series image analysis
- agricultural and forest monitoring
- SAR-based features
- optical-based features
- land-use and land-cover change classification
- usage of the analysis-ready image collections and cloud computing services
- geospatial data analysis for change detection
Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Detection of Electromagnetic Seismic Precursors from Swarm Data by An Enhanced Martingale Analytics
Authors: Yaxin Bi; Shane Harrigan; MingJun Huang; Christopher Cillian O’Neill; Wei Zhai; Jianbao Sun; Xuemin Zhang
Affiliation: Ulster University
Abstract: The detection of seismic activity precursors as part of an alarm system will provide opportunities for minimization of the social and economic impact caused by earthquakes. It has long been envisaged and a growing body of empirical evidence suggest as much that the Earth’s electromagnetic field could contain precursors to seismic events. The ability to capture and monitor electromagnetic field activity has increased in the past years as more sensors and methodologies emerge. Missions such as Swarm have enabled researchers to access near-continuous observations of electromagnetic activity at second intervals allowing for more detailed and exciting studies. In this paper, we present an approach designed to detect precursor anomalies in electromagnetic field data from Swarm satellites and initial analysis results. This works towards developing a continuous and effective monitoring system of seismic activities based on SWARM measurements and tools. We develop an enhanced form a probabilistic model based on the Martingale probability theories that allow for testing the null hypothesis to indicate abnormal changes in electromagnetic field activity. We evaluate this enhanced approach in two experiments. Firstly, we perform a quantitative comparison on well-understood and popular benchmark datasets alongside the conventional approach. We find that the enhanced version produces more accurate anomaly detections overall. Secondly, we use three case studies of seismic activity (namely earthquakes in Mexico, Greece, and Croatia) to assess our approach and the results show that our method can detect anomalous phenomena in the electromagnetic data.
Title: Transformers for Remote Sensing: A Systematic Review and Meta-analysis
Authors: Ruikun Wang; Lei Ma; Guangjun He; Brian Alan Johnson; Ziyun Yan; Ming Chang; Ying Liang
Affiliation: Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
Abstract: Research on transformers in remote sensing (RS), which started to increase after 2021, is facing the problem of relative lack of review. To understand the trends of transformers in RS, we undertook a meta-analysis of the major research on transformers over the past two years by dividing the application of transformers into 8 domains: land use/ land cover (LULC) classification, segmentation, fusion, change detection, object detection, object recognition, registration, and others. Quantitative results show that transformers achieve higher accuracy in LULC classification and fusion, with more stable performance on segmentation and object detection. Combining the analysis results on LULC classification and segmentation, we have found that transformers need more parameters than convolutional neural networks(CNNs). Additionally, further research is also needed regarding inference speed to improve transformers' performance. It was determined that the most common application scenes for transformers in our database are urban, farmland, and water bodies. We also found that transformers are employed in the natural sciences such as agriculture and environmental protection rather than the humanities or economics. Finally, this work summarizes the analysis results of transformers in remote sensing obtained during the research process and provides a perspective on future directions of development.
Title: Mapping the Green Urban: A Comprehensive Review of Materials and Machine Learning Methods in Green Infrastructure Analysis
Authors: Dino Dobrinić, PhD, Associate Professor Mario Miler, Full Professor Damir Medak
Affiliation: University of Zagreb Faculty of Geodesy, Croatia, Zagreb
Abstract: Green infrastructure plays a pivotal role in sustainable urban development, yet effective mapping and analysis of such features require a nuanced understanding of materials and sophisticated machine learning methods. This review synthesizes the current landscape of green infrastructure mapping, focusing on the diverse range of sensors and imagery utilized, as well as the application of machine learning and deep learning techniques for classification tasks. A VOSviewer analysis of the literature on green infrastructure mapping further enhances the review's depth, presenting a bibliometric network that illuminates trends and connections within the field. Results showcase interconnections between studies, their utilized sensors, and machine/deep learning methods, offering valuable insights for researchers. Overall, this review provides a comprehensive overview of the materials and machine learning methods essential for effective green infrastructure mapping. It identifies key areas for future research and innovation, paving the way for enhanced sustainability and resilience in urban environments.