Humanitarian
37 papers with code • 0 benchmarks • 1 datasets
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HumSet
Most implemented papers
Incidents1M: a large-scale dataset of images with natural disasters, damage, and incidents
ethanweber/IncidentsDataset
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In this work, we present the Incidents1M Dataset, a large-scale multi-label dataset which contains 977, 088 images, with 43 incident and 49 place categories.
Deep-Disaster: Unsupervised Disaster Detection and Localization Using Visual Data
In this paper, inspired by the success of Knowledge Distillation (KD) methods, we propose an unsupervised deep neural network to detect and localize damages in social media images.
You Only Derive Once (YODO): Automatic Differentiation for Efficient Sensitivity Analysis in Bayesian Networks
rballester/yodo
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Sensitivity analysis measures the influence of a Bayesian network's parameters on a quantity of interest defined by the network, such as the probability of a variable taking a specific value.
Open High-Resolution Satellite Imagery: The WorldStrat Dataset -- With Application to Super-Resolution
We hereby hope to foster broad-spectrum applications of ML to satellite imagery, and possibly develop from free public low-resolution Sentinel2 imagery the same power of analysis allowed by costly private high-resolution imagery.
Prioritizing emergency evacuations under compounding levels of uncertainty
However, decision makers struggle to determine optimal evacuation policies given the chaos, uncertainty, and value judgments inherent in emergency evacuations.
HumSet: Dataset of Multilingual Information Extraction and Classification for Humanitarian Crisis Response
Timely and effective response to humanitarian crises requires quick and accurate analysis of large amounts of text data - a process that can highly benefit from expert-assisted NLP systems trained on validated and annotated data in the humanitarian response domain.
Rethinking the Event Coding Pipeline with Prompt Entailment
In this work, we propose PR-ENT, a new event coding approach that is more flexible and resource-efficient, while maintaining competitive accuracy: first, we extend an event description such as "Military injured two civilians'' by a template, e. g. "People were [Z]" and prompt a pre-trained (cloze) language model to fill the slot Z.
Learning to forecast vegetation greenness at fine resolution over Africa with ConvLSTMs
earthnet2021/earthnet-models-pytorch
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Forecasting the state of vegetation in response to climate and weather events is a major challenge.
Fine-grained Population Mapping from Coarse Census Counts and Open Geodata
jvargasmu/population_estimation
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Fine-grained population maps are needed in several domains, like urban planning, environmental monitoring, public health, and humanitarian operations.
A Transformer Framework for Data Fusion and Multi-Task Learning in Smart Cities
In this paper, a Transformer-based AI system for emerging smart cities is proposed.
