most citedRapid Damage Assessment Using Social Media Images by Combining Human and Machine Intelligence

25 citations · 26 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CV20211 cited

Mapping Vulnerable Populations with AI

Benjamin Kellenberger, John E. Vargas-Muñoz, Devis Tuia +6

Humanitarian actions require accurate information to efficiently delegate support operations. Such information can be maps of building footprints, building functions, and populatio…

cs.CL2021

HumAID: Human-Annotated Disaster Incidents Data from Twitter with Deep Learning Benchmarks

Firoj Alam, Umair Qazi, Muhammad Imran +1

Social networks are widely used for information consumption and dissemination, especially during time-critical events such as natural disasters. Despite its significantly large vol…

cs.CV2021

Robust Training of Social Media Image Classification Models for Rapid Disaster Response

Firoj Alam, Tanvirul Alam, Muhammad Imran +1

Images shared on social media help crisis managers gain situational awareness and assess incurred damages, among other response tasks. As the volume and velocity of such content ar…

cs.SI202025 cited

Rapid Damage Assessment Using Social Media Images by Combining Human and Machine Intelligence

Muhammad Imran, Firoj Alam, Umair Qazi +2

Rapid damage assessment is one of the core tasks that response organizations perform at the onset of a disaster to understand the scale of damage to infrastructures such as roads,…

cs.SI2020

CrisisBench: Benchmarking Crisis-related Social Media Datasets for Humanitarian Information Processing

Firoj Alam, Hassan Sajjad, Muhammad Imran +1

Time-critical analysis of social media streams is important for humanitarian organizations for planing rapid response during disasters. The \textit{crisis informatics} research com…