Disaster mapping from satellites: damage detection with crowdsourced point labels

• URL: http://arxiv.org/abs/2111.03693v1
• Date: Fri, 5 Nov 2021 18:32:22 GMT
• Title: Disaster mapping from satellites: damage detection with crowdsourced point labels
• Authors: Danil Kuzin, Olga Isupova, Brooke D. Simmons, Steven Reece
• Abstract summary: High-resolution satellite imagery available immediately after disaster events is crucial for response planning. Damage mapping at this scale would require hundreds of expert person-hours. Crowdsourcing and recent advances in deep learning reduces the effort needed to just a few hours in real time.
• Score: 4.511561231517167
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: High-resolution satellite imagery available immediately after disaster events is crucial for response planning as it facilitates broad situational awareness of critical infrastructure status such as building damage, flooding, and obstructions to access routes. Damage mapping at this scale would require hundreds of expert person-hours. However, a combination of crowdsourcing and recent advances in deep learning reduces the effort needed to just a few hours in real time. Asking volunteers to place point marks, as opposed to shapes of actual damaged areas, significantly decreases the required analysis time for response during the disaster. However, different volunteers may be inconsistent in their marking. This work presents methods for aggregating potentially inconsistent damage marks to train a neural network damage detector.

Related papers

• Weakly-supervised Camera Localization by Ground-to-satellite Image Registration [52.54992898069471]
  We propose a weakly supervised learning strategy for ground-to-satellite image registration. It derives positive and negative satellite images for each ground image. We also propose a self-supervision strategy for cross-view image relative rotation estimation.
  arXiv  Detail & Related papers  (2024-09-10T12:57:16Z)
• Generalizable Disaster Damage Assessment via Change Detection with Vision Foundation Model [17.016411785224317]
  We present DAVI (Disaster Assessment with VIsion foundation model), which overcomes domain disparities and detects structural damage without requiring ground-truth labels of the target region. DAVI integrates task-specific knowledge from a model trained on source regions with an image segmentation foundation model to generate pseudo labels of possible damage in the target region. It then employs a two-stage refinement process, targeting both the pixel and overall image, to more accurately pinpoint changes in disaster-struck areas.
  arXiv  Detail & Related papers  (2024-06-12T09:21:28Z)
• Towards Efficient Disaster Response via Cost-effective Unbiased Class Rate Estimation through Neyman Allocation Stratified Sampling Active Learning [11.697034536189094]
  We present an innovative algorithm that constructs Neyman stratified random sampling trees for binary classification. Our findings demonstrate that our method surpasses both passive and conventional active learning techniques. It effectively addresses the'sampling bias' challenge in traditional active learning strategies.
  arXiv  Detail & Related papers  (2024-05-28T01:34:35Z)
• CrisisMatch: Semi-Supervised Few-Shot Learning for Fine-Grained Disaster Tweet Classification [51.58605842457186]
  We present a fine-grained disaster tweet classification model under the semi-supervised, few-shot learning setting. Our model, CrisisMatch, effectively classifies tweets into fine-grained classes of interest using few labeled data and large amounts of unlabeled data.
  arXiv  Detail & Related papers  (2023-10-23T07:01:09Z)
• Early Recall, Late Precision: Multi-Robot Semantic Object Mapping under Operational Constraints in Perceptually-Degraded Environments [47.917640567924174]
  We propose the Early Recall, Late Precision (EaRLaP) semantic object mapping pipeline to solve this problem. EaRLaP was used by Team CoSTAR in DARPA Subterranean Challenge, where it successfully detected all the artifacts encountered by the team of robots. We will discuss these results and performance of the EaRLaP on various datasets.
  arXiv  Detail & Related papers  (2022-06-21T01:11:42Z)
• Mapping Network States Using Connectivity Queries [64.93225229494976]
  Can we infer all the failed components of an infrastructure network, given a sample of reachable nodes from supply nodes? We formulate this novel problem using the Minimum Description Length (MDL) principle. We evaluate our algorithm on domain-expert simulations of real networks in the aftermath of an earthquake.
  arXiv  Detail & Related papers  (2020-12-07T02:04:33Z)
• Assessing Post-Disaster Damage from Satellite Imagery using Semi-Supervised Learning Techniques [15.264481724699456]
  This paper shows a novel application of semi-supervised learning (SSL) to train models for damage assessment. We compare the performance of state-of-the-art SSL methods, including MixMatch and FixMatch, to a supervised baseline for the 2010 Haiti earthquake, 2017 Santa Rosa wildfire, and 2016 armed conflict in Syria. We show how models trained with SSL methods can reach fully supervised performance despite using only a fraction of labeled data.
  arXiv  Detail & Related papers  (2020-11-24T22:26:14Z)
• Assessing out-of-domain generalization for robust building damage detection [78.6363825307044]
  Building damage detection can be automated by applying computer vision techniques to satellite imagery. Models must be robust to a shift in distribution between disaster imagery available for training and the images of the new event. We argue that future work should focus on the OOD regime instead.
  arXiv  Detail & Related papers  (2020-11-20T10:30:43Z)
• Improving Community Resiliency and Emergency Response With Artificial Intelligence [0.05541644538483946]
  We are working towards a multipronged emergency response tool that provide stakeholders timely access to comprehensive, relevant, and reliable information. Our tool consists of encoding multiple layers of open source geospatial data including flood risk location, road network strength, inundation maps that proxy inland flooding and computer vision semantic segmentation for estimating flooded areas and damaged infrastructure. These data layers are combined and used as input data for machine learning algorithms such as finding the best evacuation routes before, during and after an emergency or providing a list of available lodging for first responders in an impacted area for first.
  arXiv  Detail & Related papers  (2020-05-28T18:05:08Z)
• RescueNet: Joint Building Segmentation and Damage Assessment from Satellite Imagery [83.49145695899388]
  RescueNet is a unified model that can simultaneously segment buildings and assess the damage levels to individual buildings and can be trained end-to-end. RescueNet is tested on the large scale and diverse xBD dataset and achieves significantly better building segmentation and damage classification performance than previous methods.
  arXiv  Detail & Related papers  (2020-04-15T19:52:09Z)
• An Attention-Based System for Damage Assessment Using Satellite Imagery [18.43310705820528]
  We present Siam-U-Net-Attn model - a multi-class deep learning model with an attention mechanism - to assess damage levels of buildings. We evaluate the proposed method on xView2, a large-scale building damage assessment dataset, and demonstrate that the proposed approach achieves accurate damage scale classification and building segmentation results simultaneously.
  arXiv  Detail & Related papers  (2020-04-14T16:37:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.