Monitoring dam failures using satellite images provides first responders with efficient management of early interventions. It is also equally important to monitor spatial and temporal changes in the inundation area to track the post-disaster recovery. On January 25th, 2019, the tailings dam of the Córrego do Feijão iron ore mine, located in Brumadinho, Brazil, collapsed. This disaster caused more than 230 fatalities and 30 missing people leading to damage on the order of multiple billions of dollars. This study uses Sentinel-2 satellite images to map the inundation area and assess and delineate the land use and land cover impacted by the dam failure. The images correspond to data captures from January 22nd (3 days before), and February 02 (7 days after the collapse). Satellite images of the region were classified for before and aftermath of the disaster implementing a machine learning algorithm. In order to have sufficient land cover types to validate the quality and accuracy of the algorithm, 7 classes were defined: mine, forest, build-up, river, agricultural, clear water, and grassland. The developed classification algorithm yielded a high accuracy (99%) for the image before the collapse. This paper determines land cover impact using two different models, 1) by using the trained network in the "after" image, and 2) by creating a second network, trained in a subset of points of the "after" image, and then comparing the land cover results of the two trained networks. In the first model, applying the trained network to the "after" image, the accuracy is still high (86%), but lower than using the second model (98%). This strategy can be applied at a low cost for monitoring and assessment by using openly available satellite information and, in case of dam collapse or with a larger budget, higher resolution and faster data can be obtained by fly-overs on the area of concern.
Universidade Católica de Brasília（巴西利亚天主教大学）；Tribunal de Contas da União（联邦审计法院）；University of Bath（巴斯大学）；Oxford University（牛津大学）
This work explores the combination of free cloud computing, free open-source software, and deep learning methods to analyse a real, large-scale problem: the automatic country-wide identification and classification of surface mines and mining tailings dams in Brazil. Locations of officially registered mines and dams were obtained from the Brazilian government open data resource. Multispectral Sentinel-2 satellite imagery, obtained and processed at the Google Earth Engine platform, was used to train and test deep neural networks using the TensorFlow 2 API and Google Colab platform. Fully Convolu…
Universidade Federal de Minas Gerais（米纳斯吉拉斯联邦大学）；Universidade Católica de Brasília（巴西利亚天主教大学）；Tribunal de Contas da União（联邦审计法院）
In this work we present BrazilDAM, a novel public dataset based on Sentinel-2 and Landsat-8 satellite images covering all tailings dams cataloged by the Brazilian National Mining Agency (ANM). The dataset was built using georeferenced images from 769 dams, recorded between 2016 and 2019. The time series were processed in order to produce cloud free images. The dams contain mining waste from different ore categories and have highly varying shapes, areas and volumes, making BrazilDAM particularly interesting and challenging to be used in machine learning benchmarks. The original catalog contains…
Fast and effective responses are required when a natural disaster (e.g., earthquake, hurricane, etc.) strikes. Building damage assessment from satellite imagery is critical before relief effort is deployed. With a pair of pre- and post-disaster satellite images, building damage assessment aims at predicting the extent of damage to buildings. With the powerful ability of feature representation, deep neural networks have been successfully applied to building damage assessment. Most existing works simply concatenate pre- and post-disaster images as input of a deep neural network without consideri…
Google AI（谷歌人工智能）；UN World Food Programme（联合国世界粮食计划署）
In all types of disasters, from earthquakes to armed conflicts, aid workers need accurate and timely data such as damage to buildings and population displacement to mount an effective response. Remote sensing provides this data at an unprecedented scale, but extracting operationalizable information from satellite images is slow and labor-intensive. In this work, we use machine learning to automate the detection of building damage in satellite imagery. We compare the performance of four different convolutional neural network models in detecting damaged buildings in the 2010 Haiti earthquake. We…
Damage assessment after natural disasters is needed to distribute aid and forces to recovery from damage dealt optimally. This process involves acquiring satellite imagery for the region of interest, localization of buildings, and classification of the amount of damage caused by nature or urban factors to buildings. In case of natural disasters, this means processing many square kilometers of the area to judge whether a particular building had suffered from the damaging factors. In this work, we develop a computational approach for an automated comparison of the same region's 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 …
Gaining timely and reliable situation awareness after hazard events such as a hurricane is crucial to emergency managers and first responders. One effective way to achieve that goal is through damage assessment. Recently, disaster researchers have been utilizing imagery captured through satellites or drones to quantify the number of flooded/damaged buildings. In this paper, we propose a mixed data approach, which leverages publicly available satellite imagery and geolocation features of the affected area to identify damaged buildings after a hurricane. The method demonstrated significant impro…
Purdue University（普渡大学）；Lockheed Martin Space（洛克希德·马丁太空公司）
When disaster strikes, accurate situational information and a fast, effective response are critical to save lives. Widely available, high resolution satellite images enable emergency responders to estimate locations, causes, and severity of damage. Quickly and accurately analyzing the extensive amount of satellite imagery available, though, requires an automatic approach. In this paper, we present Siam-U-Net-Attn model - a multi-class deep learning model with an attention mechanism - to assess damage levels of buildings given a pair of satellite images depicting a scene before and after a disa…
After a hurricane, damage assessment is critical to emergency managers for efficient response and resource allocation. One way to gauge the damage extent is to quantify the number of flooded/damaged buildings, which is traditionally done by ground survey. This process can be labor-intensive and time-consuming. In this paper, we propose to improve the efficiency of building damage assessment by applying image classification algorithms to post-hurricane satellite imagery. At the known building coordinates (available from public data), we extract square-sized images from the satellite imagery to …
Simon Fraser University（西蒙弗雷泽大学）；Texas A&M University（德克萨斯A&M大学）
This paper presents \dahitra, a novel deep-learning model with hierarchical transformers to classify building damages based on satellite images in the aftermath of natural disasters. Satellite imagery provides real-time and high-coverage information and offers opportunities to inform large-scale post-disaster building damage assessment, which is critical for rapid emergency response. In this work, a novel transformer-based network is proposed for assessing building damage. This network leverages hierarchical spatial features of multiple resolutions and captures the temporal differences in the …
Post-hurricane damage assessment is crucial towards managing resource allocations and executing an effective response. Traditionally, this evaluation is performed through field reconnaissance, which is slow, hazardous, and arduous. Instead, in this paper we furthered the idea of implementing deep learning through convolutional neural networks in order to classify post-hurricane satellite imagery of buildings as Flooded/Damaged or Undamaged. The experimentation was conducted employing a dataset containing post-hurricane satellite imagery from the Greater Houston area after Hurricane Harvey in 2…
University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）；Nanjing University of Science and Technology（南京理工大学）
Fast and effective responses are required when a natural disaster (e.g., earthquake, hurricane, etc.) strikes. Building damage assessment from satellite imagery is critical before an effective response is conducted. High-resolution satellite images provide rich information with pre- and post-disaster scenes for analysis. However, most existing works simply use pre- and post-disaster images as input without considering their correlations. In this paper, we propose a novel cross-directional fusion strategy to better explore the correlations between pre- and post-disaster images. Moreover, the da…
The Academy for Mathematics, Science, and Engineering（数学、科学与工程学院）
Natural disasters ravage the world's cities, valleys, and shores on a regular basis. Deploying precise and efficient computational mechanisms for assessing infrastructure damage is essential to channel resources and minimize the loss of life. Using a dataset that includes labeled pre- and post- disaster satellite imagery, we take a machine learning-based remote sensing approach and train multiple convolutional neural networks (CNNs) to assess building damage on a per-building basis. We present a novel methodology of interpretable deep learning that seeks to explicitly investigate the most usef…
FLNet: Flood-Induced Agriculture Damage Assessment using Super Resolution of Satellite Images
Annam.AI CoE, MoE, Indian Institute of Technology Ropar, Punjab, India（Annam.AI联合研究所、教育部、印度理工学院罗帕尔分校）；Department of Mathematical Sciences, Rajiv Gandhi Institute of Petroleum Technology, Jais, UP, India（数学科学系、拉贾吉安石油技术学院）；Department of Computer Science and Engineering, Indian Institute of Technology Ropar, Punjab, India（计算机科学与工程系、印度理工学院罗帕尔分校）
Earthquakes and tropical cyclones cause the suffering of millions of people around the world every year. The resulting landslides exacerbate the effects of these disasters. Landslide detection is, therefore, a critical task for the protection of human life and livelihood in mountainous areas. To tackle this problem, we propose a combination of satellite technology and Deep Neural Networks (DNNs). We evaluate the performance of multiple DNN-based methods for landslide detection on actual satellite images of landslide damage. Our analysis demonstrates the potential for a meaningful social impact…
Video and Image Processing Laboratory（视频与图像处理实验室）；School of Electrical Engineering（电气工程学院）；Purdue University（普渡大学）
Satellite imagery is becoming increasingly accessible due to the growing number of orbiting commercial satellites. Many applications make use of such images: agricultural management, meteorological prediction, damage assessment from natural disasters, or cartography are some of the examples. Unfortunately, these images can be easily tampered and modified with image manipulation tools damaging downstream applications. Because the nature of the manipulation applied to the image is typically unknown, unsupervised methods that don't require prior knowledge of the tampering techniques used are pref…
Benchmarking Attention Mechanisms and Consistency Regularization Semi-Supervised Learning for Post-Flood Building Damage Assessment in Satellite Images
Engineering Vellore Institute of Technology, AP Guntur, India（工程维洛雷理工学院，AP古尔图，印度）；Engineering Vellore Institute of Technology, AP Ongole, India（工程维洛雷理工学院，AP翁戈尔，印度）；Engineering Vellore Institute of Technology, AP Vijayawada, India（工程维洛雷理工学院，AP维杰亚瓦达，印度）
Lebanese University（黎巴嫩大学）；Islamic University of Lebanon（黎巴嫩伊斯兰大学）；MINES ParisTech（巴黎高科矿业学院）；National Center for Remote Sensing - CNRS（法国国家科学研究中心国家遥感中心）
Buildings classification using satellite images is becoming more important for several applications such as damage assessment, resource allocation, and population estimation. We focus, in this work, on buildings damage assessment (BDA) and buildings type classification (BTC) of residential and non-residential buildings. We propose to rely solely on RGB satellite images and follow a 2-stage deep learning-based approach, where first, buildings' footprints are extracted using a semantic segmentation model, followed by classification of the cropped images. Due to the lack of an appropriate dataset…
Timely disaster risk management requires accurate road maps and prompt damage assessment. Currently, this is done by volunteers manually marking satellite imagery of affected areas but this process is slow and often error-prone. Segmentation algorithms can be applied to satellite images to detect road networks. However, existing methods are unsuitable for disaster-struck areas as they make assumptions about the road network topology which may no longer be valid in these scenarios. Herein, we propose a CNN-based framework for identifying accessible roads in post-disaster imagery by detecting ch…
The Amazon rainforests have been suffering widespread damage, both via natural and artificial means. Every minute, it is estimated that the world loses forest cover the size of 48 football fields. Deforestation in the Amazon rainforest has led to drastically reduced biodiversity, loss of habitat, climate change, and other biological losses. In this respect, it has become essential to track how the nature of these forests change over time. Image classification using deep learning can help speed up this process by removing the manual task of classifying each image. Here, it is shown how convolut…
University of Oxford（牛津大学）；TU Munich（慕尼黑工业大学）；University of Kent（肯特大学）；LIST Luxembourg（卢森堡LIST研究所）；DFKI（德国人工智能研究中心）；TU Kaiserslautern（凯泽斯劳滕工业大学）；Czech Geological Survey（捷克地质调查局）；University of Warsaw（华沙大学）
We propose a novel approach for rapid segmentation of flooded buildings by fusing multiresolution, multisensor, and multitemporal satellite imagery in a convolutional neural network. Our model significantly expedites the generation of satellite imagery-based flood maps, crucial for first responders and local authorities in the early stages of flood events. By incorporating multitemporal satellite imagery, our model allows for rapid and accurate post-disaster damage assessment and can be used by governments to better coordinate medium- and long-term financial assistance programs for affected ar…
The Pennsylvania State University（宾夕法尼亚州立大学）；AccuWeather Inc.（AccuWeather公司）
Computers are widely utilized in today's weather forecasting as a powerful tool to leverage an enormous amount of data. Yet, despite the availability of such data, current techniques often fall short of producing reliable detailed storm forecasts. Each year severe thunderstorms cause significant damage and loss of life, some of which could be avoided if better forecasts were available. We propose a computer algorithm that analyzes satellite images from historical archives to locate visual signatures of severe thunderstorms for short-term predictions. While computers are involved in weather for…
Deep Learning for Pavement Condition Evaluation Using Satellite Imagery
Department of Civil and Environmental Engineering, University of Houston（土木与环境工程系，休斯顿大学）；College of Business, North Dakota State University（商学院，北达科他州立大学）；Department of Civil and Environmental Engineering, University of South Florida（土木与环境工程系，佛罗里达州立大学）
