A Machine learning approach for rapid disaster response based on multi-modal data. The case of housing & shelter needs

• URL: http://arxiv.org/abs/2108.00887v1
• Date: Thu, 29 Jul 2021 18:22:34 GMT
• Title: A Machine learning approach for rapid disaster response based on multi-modal data. The case of housing & shelter needs
• Authors: Karla Saldana Ochoa Tina Comes
• Abstract summary: One of the most immediate needs of people affected by a disaster is finding shelter. This paper proposes a machine learning workflow that aims to fuse and rapidly analyse multimodal data. Based on a database of 19 characteristics for more than 200 disasters worldwide, a fusion approach at the decision level was used.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Along with climate change, more frequent extreme events, such as flooding and tropical cyclones, threaten the livelihoods and wellbeing of poor and vulnerable populations. One of the most immediate needs of people affected by a disaster is finding shelter. While the proliferation of data on disasters is already helping to save lives, identifying damages in buildings, assessing shelter needs, and finding appropriate places to establish emergency shelters or settlements require a wide range of data to be combined rapidly. To address this gap and make a headway in comprehensive assessments, this paper proposes a machine learning workflow that aims to fuse and rapidly analyse multimodal data. This workflow is built around open and online data to ensure scalability and broad accessibility. Based on a database of 19 characteristics for more than 200 disasters worldwide, a fusion approach at the decision level was used. This technique allows the collected multimodal data to share a common semantic space that facilitates the prediction of individual variables. Each fused numerical vector was fed into an unsupervised clustering algorithm called Self-Organizing-Maps (SOM). The trained SOM serves as a predictor for future cases, allowing predicting consequences such as total deaths, total people affected, and total damage, and provides specific recommendations for assessments in the shelter and housing sector. To achieve such prediction, a satellite image from before the disaster and the geographic and demographic conditions are shown to the trained model, which achieved a prediction accuracy of 62 %

Related papers

• CrisisSense-LLM: Instruction Fine-Tuned Large Language Model for Multi-label Social Media Text Classification in Disaster Informatics [49.2719253711215]
  This study introduces a novel approach to disaster text classification by enhancing a pre-trained Large Language Model (LLM) Our methodology involves creating a comprehensive instruction dataset from disaster-related tweets, which is then used to fine-tune an open-source LLM. This fine-tuned model can classify multiple aspects of disaster-related information simultaneously, such as the type of event, informativeness, and involvement of human aid.
  arXiv  Detail & Related papers  (2024-06-16T23:01:10Z)
• 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)
• DeCrisisMB: Debiased Semi-Supervised Learning for Crisis Tweet Classification via Memory Bank [52.20298962359658]
  In crisis events, people often use social media platforms such as Twitter to disseminate information about the situation, warnings, advice, and support. fully-supervised approaches require annotating vast amounts of data and are impractical due to limited response time. Semi-supervised models can be biased, performing moderately well for certain classes while performing extremely poorly for others. We propose a simple but effective debiasing method, DeCrisisMB, that utilizes a Memory Bank to store and perform equal sampling for generated pseudo-labels from each class at each training.
  arXiv  Detail & Related papers  (2023-10-23T05:25:51Z)
• SpatialRank: Urban Event Ranking with NDCG Optimization on Spatiotemporal Data [55.609946936979036]
  We propose a novel spatial event ranking approach named SpatialRank. We show that SpatialRank can effectively identify the top riskiest locations of crimes and traffic accidents.
  arXiv  Detail & Related papers  (2023-09-30T06:20:21Z)
• Harnessing Diverse Data for Global Disaster Prediction: A Multimodal Framework [0.0]
  This research presents a novel multimodal disaster prediction framework. We focus on "flood" and "landslide" predictions, given their ties to meteorological and topographical factors. The model is meticulously crafted based on the available data and we also implement strategies to address class imbalance.
  arXiv  Detail & Related papers  (2023-09-28T17:36:27Z)
• SurvivalGAN: Generating Time-to-Event Data for Survival Analysis [121.84429525403694]
  Imbalances in censoring and time horizons cause generative models to experience three new failure modes specific to survival analysis. We propose SurvivalGAN, a generative model that handles survival data by addressing the imbalance in the censoring and event horizons. We evaluate this method via extensive experiments on medical datasets.
  arXiv  Detail & Related papers  (2023-02-24T17:03:51Z)
• Flood Prediction Using Machine Learning Models [0.0]
  This paper aims to reduce the extreme risks of this natural disaster by providing a prediction for floods using different machine learning models. With the outcome, a comparative analysis will be conducted to understand which model delivers a better accuracy.
  arXiv  Detail & Related papers  (2022-08-02T03:59:43Z)
• Learning from Multimodal and Multitemporal Earth Observation Data for Building Damage Mapping [17.324397643429638]
  We have developed a global multisensor and multitemporal dataset for building damage mapping. The global dataset contains high-resolution optical imagery and high-to-moderate-resolution multiband SAR data. We defined a damage mapping framework for the semantic segmentation of damaged buildings based on a deep convolutional neural network algorithm.
  arXiv  Detail & Related papers  (2020-09-14T05:04:19Z)
• 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)

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.