20 papers with code • 0 benchmarks • 0 datasets

This task has no description! Would you like to contribute one?

Most implemented papers

Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping

kushthedude/Poverty-Predictor 1 Oct 2015

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

yvesalexandre/convnet-metadata 20 Nov 2015

Mobile phone metadata is increasingly used for humanitarian purposes in developing countries as traditional data is scarce.

Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data

JiaxuanYou/crop_yield_prediction 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

GKalliatakis/DisplaceNet 3 May 2019

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.

xBD: A Dataset for Assessing Building Damage from Satellite Imagery

DIUx-xView/xview2-baseline 21 Nov 2019

xBD is the largest building damage assessment dataset to date, containing 850, 736 building annotations across 45, 362 km\textsuperscript{2} of imagery.

Tigrinya Neural Machine Translation with Transfer Learning for Humanitarian Response

translatorswb/TWB-MT 9 Mar 2020

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

firojalam/multimodal_social_media 14 Apr 2020

Multimedia content in social media platforms provides significant information during disaster events.

Rapid Response Crop Maps in Data Sparse Regions

nasaharvest/togo-crop-mask 23 Jun 2020

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

JHUAPL/EstimatingDisplacedPopulations 25 Jun 2020

We introduce a deep learning approach to perform fine-grained population estimation for displacement camps using high-resolution overhead imagery.

Clustering of Social Media Messages for Humanitarian Aid Response during Crisis

swatipadhee/Crisis-Aid-Terms 23 Jul 2020

Social media has quickly grown into an essential tool for people to communicate and express their needs during crisis events.