GIS & Remote Sensing Analyst — Climate Resilience / Coastal Restoration (AI-Assisted, SF Bay)

Upwork is hiring a GIS & Remote Sensing Analyst for a climate resilience project focused on tidal marsh restoration in San Francisco Bay. The role involves data acquisition, spatial processing, and visualization using AI tools and Python. Candidates must have a Master's degree in GIS or related fields, strong skills in QGIS/ArcGIS, and experience with LiDAR and flood analysis. This is independent contractor work that can be done from anywhere.
TITLE: GIS & Remote Sensing Analyst — Climate Resilience / Coastal Restoration (AI-Assisted, SF Bay)

I am seeking an experienced GIS and remote sensing analyst for an ongoing scientific and capital markets project on tidal marsh restoration and flood risk certification in San Francisco Bay. This is independent contractor work done in close collaboration with me and AI tools (Claude). I provide near-complete analytical frameworks, Python pipelines, and detailed technical direction. Your job is skilled execution: data acquisition, spatial processing, and visualization. This is publishable-quality work at the intersection of environmental science, geospatial analysis, and climate finance — not data entry.

THE PROJECT

We are building a flood risk certification methodology for three tidal marsh restoration sites: Sears Point/Highway 37 (North Bay), Richmond Shoreline (East Bay), and South Bay Salt Ponds/Ravenswood (San Mateo County). The methodology integrates federal LiDAR elevation data, USGS CoSMoS flood hazard rasters, and SF Bay wetland monitoring data to produce site-specific Expected Annual Loss probability distributions for insurance underwriting and green bond issuance. Python pipelines implementing the Taylor-Burns et al. 2024, Nederhoff et al. 2025, and Barnard et al. 2019 methodologies are already built and running — you are providing the geospatial inputs that feed them.

SPECIFIC TASKS

1. Download and process LiDAR DEMs from NOAA Digital Coast / USGS National Map. Apply LEAN vegetation bias correction, produce bare-earth DEMs in NAVD88.

2. Download CoSMoS flood depth rasters (9 scenarios: 3 return periods x 3 SLR levels) from USGS ScienceBase. Extract mean flood depths for three site bounding boxes — 27 numbers total.

3. Habitat zone classification in QGIS using normalized tidal elevation. Produce five-class maps with professional cartographic output.

4. Elevation change analysis — difference rasters between temporal LiDAR datasets to derive accretion rates.

5. 3D visualization — perspective renders and animations showing flood inundation scenarios (baseline vs. restored marsh) at multiple sea level rise levels. Presentation-quality for institutional investors, underwriters, and regulators.

6. Ongoing spatial analysis as the project scales to additional Bay sites.

REQUIRED

— Master's degree in GIS, Remote Sensing, Environmental Science, or Geography

— Strong QGIS and/or ArcGIS Pro — habitat classification, raster algebra, cartographic output

— Experience with USGS 3DEP LiDAR (LAZ/LAS, GeoTIFF DEMs)

— Python: rasterio, numpy, pandas, scipy, matplotlib

— Experience with NOAA Digital Coast, USGS ScienceBase, National Map portals

— Demonstrated AI tool use (Claude, ChatGPT, Copilot) in professional GIS workflow

— 3D visualization: Blender, ArcGIS Pro 3D, QGIS 3D, or equivalent

— Clear written English and ability to flag anomalies precisely

PREFERRED

— Tidal/coastal wetland datasets, tidal datums (NAVD88, MHHW, MSL)

— Coastal hydrodynamic model outputs (Delft3D, CoSMoS)

— Background in training or instruction

— Experience with US federal data portals from outside the United States

ENGAGEMENT

Initial task package: 20-35 hours across three sites. Strong potential for ongoing work through 2026-2027 as the project scales. Candidates who can bring vetted colleagues under their coordination for surge capacity are strongly preferred.

Seeking candidates based outside the United States. Target rate: $15-30/hr.

TO APPLY — answer all six questions. Incomplete applications will not be reviewed.

1. Describe a coastal, wetland, or elevation change project you have completed — data used, output produced, end use.

2. Provide one 3D visualization or animation example (image, link, or file).

3. Describe specifically how you use AI tools in your GIS workflow.

4. Describe your computing setup: OS, RAM, and how you handle large raster datasets. Note: LiDAR downloads may reach 50GB+ per site.

5. Do you have colleagues with comparable skills who could work under your coordination as needed? If so, describe briefly.

6. Hourly rate and country of residence.