In the past few years, geospatial information created by volunteers and facilitated by social
networks has become a promising data source in time-critical situations. Emergency …

Disaster response systems utilizing device analytics can help rescue teams to identify
needs, prioritize responses, and increase response efficiency. Machine Learning (ML) refers …

In recent years, using social media images to assess natural disasters and the structural
damage they cause has become an increasingly prevalent research approach. Particularly …

Effective decision-supporting visualization is critical for strategic, tactic, and operational
management before and during a large-scale climate or extreme weather emergency. Most …

Traditional data collection methods such as remote sensing and field surveying often fail to
offer timely information during or immediately following disaster events. Social sensing …

Cost-effective on-demand computing resources can help to process the increasing number
of large, diverse datasets generated from smart internet-enabled technology, such as …

Formal response organizations perform rapid damage assessments after natural and human-
induced disasters to measure the extent of damage to infrastructures such as roads, bridges …

The recent advances of mobile sensing and artificial intelligence (AI) have brought new
revolutions in disaster response applications. One example is disaster scene assessment …

Technological advances, especially in remote sensing, GPS sensors, and computer vision
and camera-based tracking, enable the collection of spatio-temporal data on an …

This paper proposes a novel demo application named CityScouter that utilizes multimodal
data to analyze various aspects of urban characteristics quantitatively. Existing studies have …