Responsible AI for Earth Observation
The convergence of artificial intelligence (AI) and Earth observation (EO) technologies has brought geoscience and remote sensing into an era of unparalleled capabilities. AI's transformative impact on data analysis, particularly derived from EO platforms, holds great promise in addressing global challenges such as environmental monitoring, disaster response and climate change analysis. However, the rapid integration of AI necessitates a careful examination of the responsible dimensions inherent in its application within these domains. In this paper, we represent a pioneering effort to systematically define the intersection of AI and EO, with a central focus on responsible AI practices. Specifically, we identify several critical components guiding this exploration from both academia and industry perspectives within the EO field: AI and EO for social good, mitigating unfair biases, AI security in EO, geo-privacy and privacy-preserving measures, as well as maintaining scientific excellence, open data, and guiding AI usage based on ethical principles. Furthermore, the paper explores potential opportunities and emerging trends, providing valuable insights for future research endeavors.
Code (0)
등록된 구현이 없습니다.
Tasks
Disaster ResponseEarth ObservationPrivacy PreservingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
OlmoEarth: Stable Latent Image Modeling for Multimodal Earth Observation
Earth observation data presents a unique challenge: it is spatial like images, sequential like video or text, and highly multimodal. We present OlmoEarth: a multimodal, spatio-temporal foundation model that employs a nov…
Self-Supervised LearningTowards LLM Agents for Earth Observation
Earth Observation (EO) provides critical planetary data for environmental monitoring, disaster management, climate science, and other scientific domains. Here we ask: Are AI systems ready for reliable Earth Observation? …
ArticlesEarth ObservationManagementMulti-strip observation scheduling problem for ac-tive-imaging agile earth observation satellites
Active-imaging agile earth observation satellite (AI-AEOS) is a new generation agile earth observation satellite (AEOS). With renewed capabilities in observation and active im-aging, AI-AEOS improves upon the observation…
Earth ObservationSchedulingMachine Learning Information Fusion in Earth Observation: A Comprehensive Review of Methods, Applications and Data Sources
This paper reviews the most important information fusion data-driven algorithms based on Machine Learning (ML) techniques for problems in Earth observation. Nowadays we observe and model the Earth with a wealth of observ…
BIG-bench Machine LearningEarth ObservationInnovations in the field of on-board scheduling technologies
Space missions are characterized by long distances, difficult or unavailable communication and high operating costs. Moreover, complexity has been constantly increasing in recent years. For this reason, improving the aut…
Earth ObservationScheduling