Deep Learning for Environmental Remote Sensing

Dear Colleagues,

Machine learning (ML) applied to environmental remote sensing can help support many of the United Nations’ Sustainable Development Goals (SDGs), including life on land (SDG #15), life below water (SDG #14), water governance (SDG #6), zero hunger (SDG #2), and climate change mitigation as well as adaptation (SDG #13), to name a few. Combined with Earth observation and environmental science, data science and ML models can serve many different areas, with relevant applications in ecology, agriculture, forestry, climate modeling, and disaster responses. Recent successes in deep learning (DL) are providing effective tools with which to make advances in many of these domains; however, impactful research and deployment must be performed responsibly and with quantifiable impacts as well as actionable interpretation.

This Special Issue aims to gather cutting-edge contributions using deep learning to analyze remote sensing data for environmental applications. Contributions are accepted in different areas of application, including, but not limited to, environmental studies, agroecology, agroforestry, water management, biodiversity assessment and restoration, forest disturbances, natural resources mapping, disaster management, etc., and can offer methodological contributions including modeling, deep learning architecture search, remote sensing data engineering for DL, benchmarking, open access datasets, etc. Studies reporting on deep learning methods applied to (multitemporal) active or passive remote sensors’ data, LiDAR, airborne platforms, drones, and terrestrial vehicles are welcome, as are papers describing techniques exploiting various sources, such as multimodal and multiscale data fusion. Contributions can be submitted in various forms, such as research papers, review papers, and comparative analyses.

Dr. Laure Berti-Equille
Dr. Dino Ienco
Dr. Cássio Fraga Dantas
Guest Editors

Manuscript Submission Information

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