Disaster Response

20 papers with code • 1 benchmarks • 7 datasets

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Most implemented papers

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.

Improving Object Detection, Multi-object Tracking, and Re-Identification for Disaster Response Drones

mlvlab/drone_ai_challenge 5 Jan 2022

In the second approach, although DeepSORT only processes a quarter of all frames due to hardware and time limitations, our model with DeepSORT (42. 9%) outperforms FairMOT (71. 4%) in terms of recall.

SpaceNet 6: Multi-Sensor All Weather Mapping Dataset

burakekim/MTL_homoscedastic_SRB 14 Apr 2020

The dataset and challenge focus on mapping and building footprint extraction using a combination of these data sources.

Twitter as a Lifeline: Human-annotated Twitter Corpora for NLP of Crisis-related Messages

konstapo/2022-fake-news-mediaeval-task LREC 2016

Microblogging platforms such as Twitter provide active communication channels during mass convergence and emergency events such as earthquakes, typhoons.

From Satellite Imagery to Disaster Insights

GogulaK/Disaster-Impact-Prediction-using-Aerial-Satellite-Imagery 17 Dec 2018

The use of satellite imagery has become increasingly popular for disaster monitoring and response.

Bayesian Heatmaps: Probabilistic Classification with Multiple Unreliable Information Sources

OxfordML/heatmap_expts 5 Apr 2019

Such applications depend on classifying the situation across a region of interest, which can be depicted as a spatial "heatmap".

On Identifying Hashtags in Disaster Twitter Data

JRC1995/Tweet-Disaster-Keyphrase 5 Jan 2020

Moreover, only a small number of tweets that contain actionable hashtags are useful for disaster response.

Uneven Coverage of Natural Disasters in Wikipedia: the Case of Flood

javirandor/disasters-wikipedia-floods 23 Jan 2020

We also note how coverage of floods in countries with the lowest income, as well as countries in South America, is substantially lower than the coverage of floods in middle-income countries.

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.

The Multi-Temporal Urban Development SpaceNet Dataset

avanetten/avanetten.github.io CVPR 2021

Each building is assigned a unique identifier (i. e. address), which permits tracking of individual objects over time.