PulseSatellite: A tool using human-AI feedback loops for satellite image analysis in humanitarian contexts

利用Segment Anything模型从卫星影像中识别难民营建筑（SAM4Refugee）以支持人道主义行动

Leveraging Segment Anything Model in Identifying Buildings within Refugee Camps (SAM4Refugee) from Satellite Imagery for Humanitarian Operations

本研究利用SAM及其轻量级适配版本SAM-Adapter从卫星影像中提取难民营建筑，在数据有限时优于U-Net和Transformer，并发现超分辨率放大可提升性能及加速收敛，代码已开源。

Automatic Quantification of Settlement Damage using Deep Learning of Satellite Images

Humanitarian disasters and political violence cause significant damage to our living space. The reparation cost to homes, infrastructure, and the ecosystem is often difficult to quantify in real-time. Real-time quantification is critical to both informing relief operations, but also planning ahead for rebuilding. Here, we use satellite images before and after major crisis around the world to train a robust baseline Residual Network (ResNet) and a disaster quantification Pyramid Scene Parsing Network (PSPNet). ResNet offers robustness to poor image quality and can identify areas of destruction …

Generating Interpretable Poverty Maps using Object Detection in Satellite Images

Accurate local-level poverty measurement is an essential task for governments and humanitarian organizations to track the progress towards improving livelihoods and distribute scarce resources. Recent computer vision advances in using satellite imagery to predict poverty have shown increasing accuracy, but they do not generate features that are interpretable to policymakers, inhibiting adoption by practitioners. Here we demonstrate an interpretable computational framework to accurately predict poverty at a local level by applying object detectors to high resolution (30cm) satellite images. Usi…

xBD: A Dataset for Assessing Building Damage from Satellite Imagery

We present xBD, a new, large-scale dataset for the advancement of change detection and building damage assessment for humanitarian assistance and disaster recovery research. Natural disaster response requires an accurate understanding of damaged buildings in an affected region. Current response strategies require in-person damage assessments within 24-48 hours of a disaster. Massive potential exists for using aerial imagery combined with computer vision algorithms to assess damage and reduce the potential danger to human life. In collaboration with multiple disaster response agencies, xBD prov…

Copernicus卫星在灾害响应中的潜力：从Sentinel-1和Sentinel-2中获取建筑损毁信息

The Potential of Copernicus Satellites for Disaster Response: Retrieving Building Damage from Sentinel-1 and Sentinel-2

本文探讨了Copernicus计划的中分辨率地球观测图像在建筑损毁评估中的应用，引入了xBD-S12数据集，展示了在多种灾害场景中通过Sentinel-1和Sentinel-2图像能够有效检测和映射建筑损毁，尽管地面采样距离为10米。