Foreword to the Special Issue on Computer Vision-Based Approaches for Earth Observation

The five papers in this special section focus on computer vision-based approaches for Earth observation. These papers followed a series of events promoting works at the interface between computer vision and remote sensing: the special sessions organized at the Living Planet Symposium1 and the Computer Vision and Pattern Recognition (CVPR) conference (the EarthVision2 and Computer Vision for Global Challenges3 workshops). These sessions aimed at fostering collaboration between the computer vision and earth observation communities to boost automated interpretation of remotely sensed data. They also aimed at raising awareness inside the computer vision community for this highly challenging and quickly evolving field of research with a big impact on human society, economy, industry, and the planet. Submissions were invited from all areas of computer vision and image analysis relevant for, or applied to environmental remote sensing and were not limited to the papers presented at the events above. The papers retained in this special issue reflect the high variety of automatic image analysis in remote sensing

Report on the 2022 IEEE Geoscience and Remote Sensing Society Data Fusion Contest: Semisupervised learning

The Image Analysis and Data Fusion (IADF) Technical Committee (TC) of the IEEE Geoscience and Remote Sensing Society (GRSS) has been organizing the annual Data Fusion Contest (DFC) since 2006. The contest promotes the development of methods for extracting geospatial information from large-scale, multisensor, multimodal, and multitemporal data. It aims to propose new problem settings that are challenging to address with existing techniques and to establish new benchmarks for scientific challenges in remote sensing image analysis [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19]

Deep learning and earth observation to support the sustainable development goals: Current approaches, open challenges, and future opportunities

The synergistic combination of deep learning (DL) models and Earth observation (EO) promises significant advances to support the Sustainable Development Goals (SDGs). New developments and a plethora of applications are already changing the way humanity will face the challenges of our planet. This article reviews current DL approaches for EO data, along with their applications toward monitoring and achieving the SDGs most impacted by the rapid development of DL in EO. We systematically review case studies to achieve zero hunger, create sustainable cities, deliver tenure security, mitigate and adapt to climate change, and preserve biodiversity. Important societal, economic, and environmental implications are covered. Exciting times are coming when algorithms and Earth data can help in our endeavor to address the climate crisis and support more sustainable development