EARTHVISION 2025

Earth Observation (EO) and remote sensing are ever-growing fields of investigation where computer vision, machine learning, and signal/image processing meet. The general objective of the domain is to provide large-scale and consistent information about processes occurring at the surface of the Earth by exploiting data collected by airborne and spaceborne sensors. Earth Observation covers a broad range of tasks, from detection to registration, data mining, and multi-sensor, multi-resolution, multi-temporal, and multi-modality fusion and regression, to name just a few. It is motivated by numerous  applications such as location-based services, online mapping services, large-scale surveillance, 3D urban modeling, navigation systems, natural hazard forecast and response, climate change monitoring, virtual habitat modeling, food security, etc. The sheer amount of data calls for highly automated scene interpretation workflows.

Earth Observation and in particular the analysis of spaceborne data directly connects to 34 indicators out of 40 (29 targets and 11 goals) of the Sustainable Development Goals defined by the United Nations ( https://sdgs.un.org/goals  ). The aim of EarthVision to advance the state of the art in machine learning-based analysis of remote sensing data is thus of high relevance. It also connects to other immediate societal challenges such as monitoring of forest fires and other natural hazards, urban growth, deforestation, and climate change.

A non exhaustive list of topics of interest includes the following:

• Super-resolution in the spectral and spatial domain
• Hyperspectral and multispectral image processing
• Reconstruction and segmentation of optical and LiDAR 3D point clouds
• Feature extraction and learning from spatio-temporal data 
• Analysis  of UAV / aerial and satellite images and videos
• Deep learning tailored for large-scale Earth Observation
• Domain adaptation, concept drift, and the detection of out-of-distribution data
• Data-centric machine learning
• Evaluating models using unlabeled data
• Self-, weakly, and unsupervised approaches for learning with spatial data
• Foundation models and representation learning in the context of EO
• Human-in-the-loop and active learning
• Multi-resolution, multi-temporal, multi-sensor, multi-modal processing
• Fusion of machine learning and physical models
• Explainable and interpretable machine learning in Earth Observation applications
• Uncertainty quantification of machine-learning based prediction from EO data
• Applications for climate change, sustainable development goals, and geoscience
• Public benchmark datasets: training data standards, testing & evaluation metrics, as well as open source research and development.

All manuscripts will be subject to a double-blind review process. Accepted EarthVision papers will be included in the CVPR2024 workshop proceedings (published open access on the Computer Vision Foundation website) and submitted to IEEE for publication in IEEEXplore. Publication in IEEEXplore will be granted only if the paper meets IEEE publication policies and procedures.