The BCC Geospatial Center of the CUNY CREST Institute or BGCCCI is a collaborative satellite center created by a Memorandum of Understanding between the CUNY CREST Institute [City College of New York] and Bronx Community College of the City University of New York. The Center is built on a foundation of student centric scholarly activities delivered by an enterprising group of affiliated faculty and staff. Since 2010 and in association with an Industry consortium, activities at the Center have led to geospatial workforce skills and career pathways in geospatial technology. The Center achieves this by offering all year round training programs for participants and educators from schools and Universities [K-16] across the U.S. Interns and collaborators from local and global institutions conduct cutting-edge research on spatial analyses, satellite image analyses, machine and deep learning. Dr. Sunil Bhaskaran a Professor at the City University of New York spearheaded the development and growth of geospatial technology that laid the foundation for the launch of the Center. The multi-million dollar Center has evolved into a major intellectual hub in the region with the help of sustained funding and support by BCC-CUNY, an Industry consortium, the National Science Foundation [NSF], U.S. Department of Transportation [USDOT], the National Aeronautics Space Administration [NASA] and the New York State Department of Labor [NYSDOL].
Story of BGCCCI - IMOVIE
Tutorials for the Center
Request access to 13 tutorials by emailing Sunil_Director.Bgccci@bcc.cuny.edu.
ATE-Connects Conference Handouts
Research Areas of the Center
Image Analysis
Multi-resolution remotely sensed data acquired from Spaceborne and Airborne consist spatial and temporal information that is critical for many applications.
Spatial Analyses
Spatial analytics can assist in decision making process for a wide range of industry and applications. We develop real-world solutions for better management of human and natural resources using accurate and curated geographic data.
Machine/Deep Learning
We use Amazon Sage Maker frame work for extracting spatial features from time-series of satellite data and deploy end user solutions by using EC2 instances.
Geospatial Technology for Education
As the Geospatial Industry grows exponentially the need for skilled technicians is critical for different projects. We train participants from K-16, educators and conduct cutting-edge research with Interns across the U.S. Inquiry-based hands-on learning materials are created by using industry standard software and remotely sensed and GIS databases.
Live Global COVID-19 Cases
Live Global COVID-19 Deaths
COVID-19 Deaths vs. Poverty & Human Development Index
COVID-19 Deaths vs. Health Indicators
COVID-19 Deaths vs. Demographic Indicators
BGCCCI EVENTS BY PHOTOS - CLICK LINK BELOW
BGCCCI Interns and successfull career pathways
CURRENT RESEARCH
Our forthcoming article -- Multi-resolution Spatio-Temporal Satellite Based Analyses for Vulnerability Assessment from Flash Floods in six global cities.
Assessments of climate change events at any scale demands a synchronized and measured approach for building resilience and adaptive capacity. Given the nature of events, and its multifaceted dimensions that go beyond the physical components, pulling off assessments at city scale can be a nightmare anywhere.
We demonstrate an approach where satellite based analyses may assist in rapid vulnerability assessments We extracted critical metrics from satellite based imagery by employing per-pixel and texture based analyses on time-series of multispectral optical satellite data + Synthetic Aperture Radar (SAR) data to determine the impact of flash floods. We modelled the socio-economic component by integrating the derived data with global demographic (GPWv4) data. By using a factor analyses and weighted indexing method the vulnerability from flash floods was calculated for six major global cities with contrasting economies. The methodology is built on a data driven and computational approach that uses a combination of methods including cloud based machine learning (XBoost gradient), python, and statistical techniques. The results were validated by using reliable surrogate data. Results show a near-real time model for assessing vulnerability and calculating community vulnerability indices (CVIs) on the fly at city scales for climate change events. The rapid assessment technique has significant implications for assessing the impact of climate change events on human systems in the Global South.