Application of Satellite Remote Sensing in Geospatial Monitoring

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

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