David Saah

Director of Geospatial Analysis Lab & Associate Professor

University of California, San Francisco

United States

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David Saah is the Director of the Geospatial Analysis Lab and an Associate Professor at the University of San Francisco, United States. His research focuses on geospatial analysis, particularly in the areas of wildfire forecasting, environmental monitoring, and remote sensing technologies. Recent publications include work on cost-optimized estimation of burn probability maps, real-time monitoring of illegal gold mining, and advancements in probabilistic wildfire risk assessments.

Articles (14)

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2024

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2020

Collaborators (4)

Farrukh Chishtie

The University of British Columbia

CANADA

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Edward Kearns

Chief Data Officer

UNITED STATES

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Gary Johnson

Chief Computational Scientist

UNITED STATES

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Timothy J. Mayer

The University of Alabama

UNITED STATES

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David Saah

Research Interests

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Articles (14)

A Framework for Conducting and Communicating Probabilistic Wildland Fire Forecasts

Bridging the national data gap with Google earth engine and landsat imagery by developing annual land cover for Afghanistan

Importance Sampling for Cost-Optimized Estimation of Burn Probability Maps in Wildfire Monte Carlo Simulations

Creating near real-time alerts of illegal gold mining in the Peruvian Amazon using Synthetic Aperture Radar

Uncovering Dynamics of Global Mangrove Gains and Losses

A Near Real-Time Mapping of Tropical Forest Disturbance Using SAR and Semantic Segmentation in Google Earth Engine

Evaluating SAR Radiometric Terrain Correction Products: Analysis-Ready Data for Users

The Construction of Probabilistic Wildfire Risk Estimates for Individual Real Estate Parcels for the Contiguous United States

Mass fire behavior created by extensive tree mortality and high tree density not predicted by operational fire behavior models in the southern Sierra Nevada

Automatic Detection of Impervious Surfaces from Remotely Sensed Data Using Deep Learning

Carbon Life Cycle Assessment on California-Specific Wood Products Industries: Do Data Backup General Default Values for Wood Harvest and Processing?

Lessons Learned While Implementing a Time-Series Approach to Forest Canopy Disturbance Detection in Nepal

A Comparison of Three Temporal Smoothing Algorithms to Improve Land Cover Classification: A Case Study from NEPAL

Comparing Sentinel-1 Surface Water Mapping Algorithms and Radiometric Terrain Correction Processing in Southeast Asia Utilizing Google Earth Engine

Collaborators (4)

Farrukh Chishtie

Edward Kearns

Gary Johnson

Timothy J. Mayer

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