I can help you with:

• Designing robust GIS-integrated workflows and methodologies
• Spatial data discovery, acquisition, cleaning and visualization
• All web-mapping options available in ArcGIS Online
• Spatial analyses and app creation with Google Earth Engine and ArcGIS Pro
• 3D with LiDAR, including data processing, and analyses
• Incorporating Machine Learning/Deep Learning frameworks in ArcGIS Pro
• Introductory fire/fuel modeling knowledge

Andrew Saah is a Master of Forestry Student in the Yale School of the Environment whose prior research and work experience broadly encompassed GIS and remote sensing applications in natural resource management. Specifically, he has an extensive background in working with various forest-based LiDAR data collected a comprehensive range of sensors spanning from phone based, to space based, and everything in between. He is mainly experienced in ArcGIS Pro and ArcGIS online web mapping, ArcPy, Cloud Compare, and Google Earth Engine as well as open-source GIS packages in Python like Geo-Pandas, open3d, etc. He also has a background training and using pre trained Machine Learning and Deep Learning models for 3D object detection, land classification and 3D point segmentation.   

Some of his past research has focused on landcover mapping using satellite imagery, forest structural modeling with airborne and space-based LiDAR data products, climate risk projections, and landscape scale fire risk modeling as well as fuel modeling. Beyond natural resource management, he has a strong foundation in both vector and raster data products for epidemiological studies, urban planning, humanitarian aid, and climate change risk projections according to differing RCP pathways. 

I can help you with:

• Developing and refining spatial research questions and frameworks
• Spatial data discovery and management, including remote sensing, climate, demographic, political, etc.
• Geospatial analyses in R, QGIS, and Google Earth Engine
• Advanced analyses with vector and raster data
• Troubleshooting and improving geospatial projects
• Data visualization, cartography, and interactive mapping

Davi Bendavid is a PhD student in the School of the Environment. He has broad expertise in geography and GIS, as well as specialized expertise in applications related to land use, climate, agriculture, and ecology. His current research investigates earth system and ecological implications of conservation agriculture adoption in the Brazilian Amazon. He has broad interests in the intersection of social and environmental systems, particularly in frontier regions. 

Davi has extensive experience analyzing large geospatial datasets using R, ArcGIS, QGIS, and other software. He is particularly experienced in using remote sensing and land use datasets, alongside climate datasets and model outputs, to answer large ecological and environmental research questions, mostly in R. Outside of his main research area, he also has experience advising on the use of geospatial data in public health applications. He is passionate about developing spatially-explicit research questions and frameworks across fields, and encouraging interdisciplinary research through GIS. 

I can help you with:

• Integrating remote sensing and GIS data into your project
• Satellite data processing using Google Earth Engine and ENVI
• UAV and ROV photogrammetry database management with Agisoft
• Geospatial analysis using Python
• Geocoding, integrating API and web crawler
• Cartography and geospatial data visualization for 2D and 3D products

Aslan is a Master’s student at the Yale School of Environment. Her current research focuses on integrating various satellite products to track the interaction between climate change, urban development, and urban forests.

Aslan has rich experience in acquiring, processing, and analyzing satellite products using Google Earth Engine and ENVI. She also has expertise in managing UAV and ROV photogrammetry databases with Agisoft. In addition, she has approximately four years of experience working with GIS software and geospatial data analysis using Python and R. She is experienced in processing audible bioacoustics data using Raven Pro, Adobe Audition, and BirdNet.

I can help you with:

• Integration of GIS and remote sensing in your project
• Spatial data discovery and management
• Spatial analysis and statistics with Python and R
• Geocoding, integrating API and web crawler
• Building models and simulations for environmental and urban studies
• Interactive maps and visualizations for ScienceComm (StoryMaps, Dashboards, Canva, Survey123)

Xiaofan Shen is a Master of Environmental Science student at the Yale School of the Environment. Her current research examines how urban morphological traits influence local climate resilience. More broadly, her work bridges urban and environmental applications of geospatial science, with interests in how spatial data and modeling can inform planning, resilience, and sustainability. 

Xiaofan has extensive experience in GIS, remote sensing, and spatial data science across platforms such as ArcGIS Pro, QGIS, Google Earth Engine, and ENVI. She is skilled in spatial modeling and statistics using Python and R, and in developing interactive geospatial tools like StoryMaps and Survey123 to connect data collection with visualization and decision-making. She is particularly interested in integrating spatial networks, remote sensing, and data science methods to create practical, data-driven insights for urban and environmental planning.