10 citations · 12 across the 5 of their papers we have counts for
5 papers
DisasterInsight: A Building-Centric Benchmark for Evaluating Vision--Language Models in Disaster Response
Sara Tehrani, Yonghao Xu, Leif Haglund +3
Vision--language models (VLMs) show promise for disaster-response remote sensing, but existing benchmarks mainly emphasize scene-level or damage-centric assessment. To study this b…
VLM2GeoVec: Toward Universal Multimodal Embeddings for Remote Sensing
Emanuel Sánchez Aimar, Gulnaz Zhambulova, Fahad Shahbaz Khan +2
Satellite imagery differs from natural images in viewpoint, resolution, scale variation, and the prevalence of small objects -- demanding both region-level spatial reasoning and ho…
Comparing Next-Day Wildfire Predictability of MODIS and VIIRS Satellite Data
Justus Karlsson, Yonghao Xu, Amanda Berg +1
Multiple studies have performed next-day fire prediction using satellite imagery. Two main satellites are used to detect wildfires: MODIS and VIIRS. Both satellites provide fire ma…
On the Adversarial Vulnerabilities of Transfer Learning in Remote Sensing
Tao Bai, Xingjian Tian, Yonghao Xu +1
The use of pretrained models from general computer vision tasks is widespread in remote sensing, significantly reducing training costs and improving performance. However, this prac…
Sen2Fire: A Challenging Benchmark Dataset for Wildfire Detection using Sentinel Data
Yonghao Xu, Amanda Berg, Leif Haglund
Utilizing satellite imagery for wildfire detection presents substantial potential for practical applications. To advance the development of machine learning algorithms in this doma…