Ecoresolve provides a cloud‑based platform that uses satellite, drone and aerial imagery combined with AI‑driven analytics to produce high‑resolution, time‑series maps of coastal habitats such as mangroves, seagrasses and wetlands.
Funding
Funding not disclosed
Founders
Product
Problem
Coastal ecosystem restoration projects often lack high‑resolution, up‑to‑date spatial data, making it difficult to assess habitat conditions, prioritize interventions, and measure outcomes. This data gap hampers effective decision‑making for biodiversity conservation, carbon‑offset initiatives, and disaster risk mitigation.
Solution
Ecoresolve delivers a cloud‑based platform that combines satellite remote sensing, aerial imagery, and machine‑learning analytics to generate detailed maps of coastal habitats such as mangroves, seagrasses, and wetlands. Users can explore time‑series visualizations, quantify ecosystem services, and model restoration scenarios directly within the web interface. The platform integrates community‑sourced observations to refine model outputs and ensure relevance to local contexts. By providing standardized, exportable datasets and actionable insights, Ecoresolve enables policymakers, NGOs, and private sector actors to design, implement, and monitor nature‑based solutions with greater scientific rigor and transparency.
Target Audience
Primary users include environmental NGOs, government agencies, and private enterprises involved in coastal restoration, carbon‑credit projects, and climate‑resilience planning.
Features
- Multi‑source remote sensing data pipeline (satellite, drone, aerial) processed with AI‑driven classification algorithms for coastal vegetation and land‑cover mapping
- Temporal change detection tools that quantify habitat loss, gain, and carbon sequestration over customizable periods
- Interactive web dashboard with layer stacking, heat‑maps, and export options (GeoJSON, CSV, shapefile) for integration into GIS workflows
- Community input module allowing local stakeholders to upload field observations, which are incorporated into model training and validation
- Scenario modeling engine that simulates restoration outcomes under different planting densities, protection measures, or climate impacts
- API access for automated data retrieval and embedding of analytics into third‑party decision‑support systems