Earthvqa: Towards queryable earth via relational reasoning-based remote sensing visual question answering

J Wang, Z Zheng, Z Chen, A Ma, Y Zhong - Proceedings of the AAAI …, 2024 - ojs.aaai.org
Earth vision research typically focuses on extracting geospatial object locations and
categories but neglects the exploration of relations between objects and comprehensive …

Position: mission critical–satellite data is a distinct modality in machine learning

E Rolf, K Klemmer, C Robinson… - Forty-first International …, 2024 - openreview.net
Satellite data has the potential to inspire a seismic shift for machine learning---one in which
we rethink existing practices designed for traditional data modalities. As machine learning …

[HTML][HTML] Satellite data shows resilience of Tigrayan farmers in crop cultivation during civil war

HR Kerner, C Nakalembe, B Yeh, I Zvonkov… - Science of Remote …, 2024 - Elsevier
Abstract The Tigray War was an armed conflict that took place primarily in the Tigray region
of northern Ethiopia from November 3, 2020 to November 2, 2022. Given the importance of …

Copy-Move Forgery Detection and Question Answering for Remote Sensing Image

Z Zhang, E Zhao, Z Wan, J Nie, X Liang… - arxiv preprint arxiv …, 2024 - arxiv.org
This paper introduces the task of Remote Sensing Copy-Move Question Answering
(RSCMQA). Unlike traditional Remote Sensing Visual Question Answering (RSVQA) …

Towards more efficient agricultural practices via transformer-based crop type classification

EU Moya-Sánchez, YS Mikail, D Nyang'anyi… - arxiv preprint arxiv …, 2024 - arxiv.org
Machine learning has great potential to increase crop production and resilience to climate
change. Accurate maps of where crops are grown are a key input to a number of …