Responsible AI for Earth Observation
Reviewed by Pithpith:A26L6E3Fopen to challenge →
read the original abstract
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.
This paper has not been read by Pith yet.
Forward citations
Cited by 1 Pith paper
-
Survey on Disaster Management Datasets for Remote Sensing Based Emergency Applications
A survey providing an overview of publicly available image-based datasets for ML/DL-based disaster management pipelines covering pre-disaster, during, and post-disaster phases.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.