Humanitarian
31 papers with code • 0 benchmarks • 1 datasets
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HumSet
Most implemented papers
xBD: A Dataset for Assessing Building Damage from Satellite Imagery
DIUx-xView/xview2-baseline
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xBD is the largest building damage assessment dataset to date, containing 850, 736 building annotations across 45, 362 km\textsuperscript{2} of imagery.
Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping
We train a fully convolutional CNN model to predict nighttime lights from daytime imagery, simultaneously learning features that are useful for poverty prediction.
Modeling the Temporal Nature of Human Behavior for Demographics Prediction
Mobile phone metadata is increasingly used for humanitarian purposes in developing countries as traditional data is scarce.
Twitter as a Lifeline: Human-annotated Twitter Corpora for NLP of Crisis-related Messages
Microblogging platforms such as Twitter provide active communication channels during mass convergence and emergency events such as earthquakes, typhoons.
Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data
JiaxuanYou/crop_yield_prediction
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AAAI 2017 2017
Agricultural monitoring, especially in developing countries, can help prevent famine and support humanitarian efforts.
DisplaceNet: Recognising Displaced People from Images by Exploiting Dominance Level
Every year millions of men, women and children are forced to leave their homes and seek refuge from wars, human rights violations, persecution, and natural disasters.
Tigrinya Neural Machine Translation with Transfer Learning for Humanitarian Response
We report our experiments in building a domain-specific Tigrinya-to-English neural machine translation system.
Analysis of Social Media Data using Multimodal Deep Learning for Disaster Response
Multimedia content in social media platforms provides significant information during disaster events.
Rapid Response Crop Maps in Data Sparse Regions
A major challenge for developing crop maps is that many regions do not have readily accessible ground truth data on croplands necessary for training and validating predictive models, and field campaigns are not feasible for collecting labels for rapid response.
Estimating Displaced Populations from Overhead
We introduce a deep learning approach to perform fine-grained population estimation for displacement camps using high-resolution overhead imagery.
