Desk-AId: Humanitarian Aid Desk Assessment with Geospatial AI for Predicting Landmine Areas

• URL: http://arxiv.org/abs/2405.09444v1
• Date: Wed, 15 May 2024 15:39:35 GMT
• Title: Desk-AId: Humanitarian Aid Desk Assessment with Geospatial AI for Predicting Landmine Areas
• Authors: Flavio Cirillo, Gürkan Solmaz, Yi-Hsuan Peng, Christian Bizer, Martin Jebens,
• Abstract summary: Desk-AId uses a Geospatial AI approach specialized to landmines. The proposed system addresses the issue of having only ground-truth for confirmed hazardous areas. Experiments validate Desk-Aid in two domains for landmine risk assessment: 1) country-wide, and 2) uncharted study areas.
• Score: 2.105636394911375
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The process of clearing areas, namely demining, starts by assessing and prioritizing potential hazardous areas (i.e., desk assessment) to go under thorough investigation of experts, who confirm the risk and proceed with the mines clearance operations. This paper presents Desk-AId that supports the desk assessment phase by estimating landmine risks using geospatial data and socioeconomic information. Desk-AId uses a Geospatial AI approach specialized to landmines. The approach includes mixed data sampling strategies and context-enrichment by historical conflicts and key multi-domain facilities (e.g., buildings, roads, health sites). The proposed system addresses the issue of having only ground-truth for confirmed hazardous areas by implementing a new hard-negative data sampling strategy, where negative points are sampled in the vicinity of hazardous areas. Experiments validate Desk-Aid in two domains for landmine risk assessment: 1) country-wide, and 2) uncharted study areas). The proposed approach increases the estimation accuracies up to 92%, for different classification models such as RandomForest (RF), Feedforward Neural Networks (FNN), and Graph Neural Networks (GNN).

Related papers

• Combining Observational Data and Language for Species Range Estimation [63.65684199946094]
  We propose a novel approach combining millions of citizen science species observations with textual descriptions from Wikipedia. Our framework maps locations, species, and text descriptions into a common space, enabling zero-shot range estimation from textual descriptions. Our approach also acts as a strong prior when combined with observational data, resulting in more accurate range estimation with less data.
  arXiv  Detail & Related papers  (2024-10-14T17:22:55Z)
• EAIRiskBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents [47.69642609574771]
  Embodied artificial intelligence (EAI) integrates advanced AI models into physical entities for real-world interaction. Foundation models as the "brain" of EAI agents for high-level task planning have shown promising results. However, the deployment of these agents in physical environments presents significant safety challenges. This study introduces EAIRiskBench, a novel framework for automated physical risk assessment in EAI scenarios.
  arXiv  Detail & Related papers  (2024-08-08T13:19:37Z)
• Enhancing Worldwide Image Geolocation by Ensembling Satellite-Based Ground-Level Attribute Predictors [4.415977307120618]
  We examine the challenge of estimating the location of a single ground-level image in the absence of GPS or other location metadata. We introduce a novel metric, Recall vs Area, which measures the accuracy of estimated distributions of locations. We then examine an ensembling approach to global-scale image geolocation, which incorporates information from multiple sources.
  arXiv  Detail & Related papers  (2024-07-18T19:15:52Z)
• Visual Environment Assessment for Safe Autonomous Quadrotor Landing [8.538463567092297]
  We present a novel approach for detection and assessment of potential landing sites for safe quadrotor landing. Our solution efficiently integrates 2D and 3D environmental information, eliminating the need for external aids such as GPS. Our approach runs in real-time on quadrotors equipped with limited computational capabilities.
  arXiv  Detail & Related papers  (2023-11-16T18:02:10Z)
• RELand: Risk Estimation of Landmines via Interpretable Invariant Risk Minimization [34.655432946895935]
  Landmines remain a threat to war-affected communities for years after conflicts have ended. Humanitarian demining operations begin by collecting relevant information from the sites to be cleared. We propose RELand system to support these tasks, which consists of three major components.
  arXiv  Detail & Related papers  (2023-11-06T14:07:47Z)
• AB2CD: AI for Building Climate Damage Classification and Detection [0.0]
  We explore the implementation of deep learning techniques for precise building damage assessment in the context of natural hazards. We tackle the challenges of generalization to novel disasters and regions while accounting for the influence of low-quality and noisy labels. Our research findings showcase the potential and limitations of advanced AI solutions in enhancing the impact assessment of climate change-induced extreme weather events.
  arXiv  Detail & Related papers  (2023-09-03T03:37:04Z)
• Dwelling Type Classification for Disaster Risk Assessment Using Satellite Imagery [3.88838725116957]
  Vulnerability and risk assessment of neighborhoods is essential for effective disaster preparedness. Existing traditional systems, due to dependency on time-consuming and cost-intensive field surveying, do not provide a scalable way to decipher warnings and assess the precise extent of the risk at a hyper-local level. In this work, machine learning was used to automate the process of identifying dwellings and their type to build a potentially more effective disaster vulnerability assessment system.
  arXiv  Detail & Related papers  (2022-11-16T03:08:15Z)
• A General Purpose Neural Architecture for Geospatial Systems [142.43454584836812]
  We present a roadmap towards the construction of a general-purpose neural architecture (GPNA) with a geospatial inductive bias. We envision how such a model may facilitate cooperation between members of the community.
  arXiv  Detail & Related papers  (2022-11-04T09:58:57Z)
• Embedding Earth: Self-supervised contrastive pre-training for dense land cover classification [61.44538721707377]
  We present Embedding Earth a self-supervised contrastive pre-training method for leveraging the large availability of satellite imagery. We observe significant improvements up to 25% absolute mIoU when pre-trained with our proposed method. We find that learnt features can generalize between disparate regions opening up the possibility of using the proposed pre-training scheme.
  arXiv  Detail & Related papers  (2022-03-11T16:14:14Z)
• Triggering Failures: Out-Of-Distribution detection by learning from local adversarial attacks in Semantic Segmentation [76.2621758731288]
  We tackle the detection of out-of-distribution (OOD) objects in semantic segmentation. Our main contribution is a new OOD detection architecture called ObsNet associated with a dedicated training scheme based on Local Adversarial Attacks (LAA) We show it obtains top performances both in speed and accuracy when compared to ten recent methods of the literature on three different datasets.
  arXiv  Detail & Related papers  (2021-08-03T17:09:56Z)
• Collaborative Training between Region Proposal Localization and Classification for Domain Adaptive Object Detection [121.28769542994664]
  Domain adaptation for object detection tries to adapt the detector from labeled datasets to unlabeled ones for better performance. In this paper, we are the first to reveal that the region proposal network (RPN) and region proposal classifier(RPC) demonstrate significantly different transferability when facing large domain gap.
  arXiv  Detail & Related papers  (2020-09-17T07:39:52Z)

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.