全球暴露与物理脆弱性动态的开放式全球映射：机器学习与遥感在最不发达国家的应用

Aug 5, 2026

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Amid the 2015–2030 Sendai Framework midterm, many countries struggle to monitor climate and disaster risk due to costly, large-scale surveys of exposure and physical vulnerability, leaving risks unmitigated under intensifying climate change. This work maps exposure and physical vulnerability dynamics using machine learning with time-series remote sensing from publicly available Sentinel-1 SAR GRD and Sentinel-2 MSI. It introduces OpenSendaiBench (47 countries, mostly LDCs) trained with ResNet-50 models and demonstrates mapping Dhaka, Bangladesh informal constructions to support large-scale risk auditing over time.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages8

Reading Time20 min

AccessFree to download

Introduction

Background and challenges in monitoring risk

Data limitations and bias in exposure-vulnerability datasets

Motivation for Sentinel-based, ML-driven vulnerability mapping

The “OpenSendaiBench” dataset

Dataset scope and availability

GLOBAL MAPPING OF EXPOSURE AND PHYSICAL VULNERABILITY DYNAMICS IN LEAST DEVELOPED COUNTRIES USING REMOTE SENSING AND MACHINE LEARNING arXiv :2404 .0 1748v 1 [ cs .LG] 2 Apr 2024 Joshua Dimasaka UKRI CDT in AI for Environmental Risks Centre for Risk in the Built Environment Department of Architecture, University of Cambridge Cambridge, United Kingdom [jtd33@cam.ac.uk](jtd33@cam.ac.uk) Emily So Centre for Risk in the Built Environment Department of Architecture, University of Cambridge Cambridge, United Kingdom[ekms2@cam.ac.uk](ekms2@cam.ac.uk) Christian Geiß German Aerospace Center (DLR) Institute of Geography, University of Bonn Bonn, Germany [christian.geiss@dlr.de](christian.geiss@dlr.de) ABSTRACT As the world marked the midterm of the Sendai Framework for Disaster Risk Reduction 2015-2030, many countries are still struggling to monitor their climate and disaster risk because of the expensive large-scale survey of the distribution of exposure and physical vulnerability and, hence, are not on track in reducing risks amidst the intensifying effects of climate change. We present an ongoing effort in mapping this vital information using machine learning and time-series remote sensing from publicly available Sentinel-1 SAR GRD and Sentinel-2 Harmonized MSI. We introduce the development of “OpenSendaiBench” consisting of 47 countries wherein most are least developed (LDCs), trained ResNet-50 deep learning models, and demonstrated the region of Dhaka, Bangladesh by mapping the distribution of its informal constructions. As a pioneering effort in auditing global disaster risk over time, this paper aims to advance the area of large-scale risk quantification in informing our collective long-term efforts in reducing climate and disaster risk. 1 INTRODUCTION A global concern on the increasing frequency and intensity of climate disasters, the exacerbating effects of climate change, and the higher rate of increase of exposed human settlements despite a decrease in their vulnerability urged the international community to jointly develop the Sendai Framework for Disaster Risk Reduction (SFDRR) 2015-2030 (UNISDR, 2015) . However, in its 2023 midterm review, the United Nations reported that ”a lack of quality, interoperable, or accessible data” to quantify risk as a product of hazard, exposure, and vulnerability remains a challenge, especially in many least developed countries (LDCs) where data-collection tools have become inequitably unaffordable (UNDRR, 2023) . In particular, the expensive large-scale operation to standardize exposure datasets (e.g., human settlements) across countries with different and incomplete physical vulnerability characteristics (e.g., building material and construction type) has remained the primary bottleneck to providing a reliable understanding and audit of the evolving climate and disaster risk landscape globally (So, 2023) . Early efforts in developing large-scale exposure datasets were able to map the distribution of human settlements and their physical vulnerabilities (Gamba et al., 2012 ; Huyck et al., 2019), which have been the basis of several global assessment reports (UNDRR, 2013 ; 2015 ; 2019 ; 2022) . Unfortunately, these datasets contain limited generalizability and inherent biases that favor developed countries. Specifically, LDCs have different and non-standard vulnerability characteristics because of the ubiquity of informal settlements and different construction methodologies (Gunasekera et al., 2015 ; Silva et al., 2022) and are increasingly outdated because of rapid urbanization (So, 2023) . Furthermore, the theme of most data-driven efforts (Esch et al., 2022 ; Sirko et al., 2021) focuses on mere detection of buildings (i.e., a simple binary task to estimate the presence or absence of a building as a geometry feature or a land use class that is inferred from satellite imagery) . Despite several geospatial dasymetric efforts using digital elevation (DEM) and

FAQs

为什么在最不发达国家监测灾害风险仍然困难？

因为缺乏高质量、可互操作或可获取的数据，且用于标准化暴露数据集与物理脆弱性特征的跨国大规模调查成本高，导致难以量化风险并跟上气候变化带来的风险加剧。

本文如何使用遥感与机器学习来进行暴露与物理脆弱性动态制图？

通过时间序列遥感数据与机器学习方法，使用公开的 Sentinel-1 SAR GRD 与 Sentinel-2 Harmonized MSI，并在多像素、多分辨率实现中采用 ResNet-50 深度学习模型来映射分布。

OpenSendaiBench 数据集包含哪些关键内容？

OpenSendaiBench 是一个包含 47 个国家的基准数据集，其中大多数为最不发达国家（LDCs）；文中还给出了数据集的发布与文件夹结构，用于支持基于遥感的暴露与脆弱性审计建模。

Aug 5, 2026

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