arXiv Computer Vision
Sep 15

Global-Local Contextual Progressive Expansion Network for Martian Landslide Segmentation in Multimodal Remote Sensing Imagery

arXiv:2609.13332v1 Announce Type: new Abstract: Automated landslide segmentation on Mars is one of the important tasks for understanding its surface processes, and all will aid in future space explor...

By Leo Thomas Ramos, Sidike Paheding, Abel A. Reyes-Angulo, Rajaneesh A., Sajinkumar K. S., Angel D. Sappa, Thomas Oommen
arXiv Computer Vision
3d ago

OpenEarth-OVS44: A Multimodal Remote Sensing Dataset for Open-Vocabulary Segmentation

OpenEarth-OVS44 is a new multimodal remote sensing dataset featuring 290,587 expert‑verified segments from 2,832 RGB images and 1,341 RGB‑SAR pairs at sub‑meter resolution, covering 40 cities in France, Japan, and the United States. It adopts a unified 44‑class taxonomy organized into nine base categories and offers three evaluation tracks: an RGB‑only fine‑grained benchmark, a multimodal track with synthetic cloudy conditions for weather‑robust evaluation, and a held‑out cross‑region track. Experiments show that training on OpenEarth-OVS44 improves fine‑grained segmentation performance, achieving up to 7.1 mIoU points on unseen categories and 27.6 mIoU points on seen categories compared to the strongest training‑free baseline.

By Yimin Wei, Hongruixuan Chen, Hao Zheng, Hao Liu, Jian Song, Yuting Zhu, Junshi Xia, Naoto Yokoya
arXiv AI
Jun 19

TerraMind: Large-Scale Generative Multimodality for Earth Observation

arXiv:2504. 11171v5 Announce Type: replace-cross Abstract: We present TerraMind, the first any-to-any generative, multimodal foundation model for Earth observation (EO).

By Johannes Jakubik, Felix Yang, Benedikt Blumenstiel, Erik Scheurer, Rocco Sedona, Stefano Maurogiovanni, Jente Bosmans, Nikolaos Dionelis, Valerio Marsocci, Niklas Kopp, Rahul Ramachandran, Paolo Fraccaro, Thomas Brunschwiler, Gabriele Cavallaro, Juan Bernabe-Moreno, Nicolas Long\'ep\'e