Earth Data API

Closed alpha testing

Data Enrichment Protocols

Raw data collected from the Trouble Map, Event Engine, and Satellite Module undergoes an automated post-processing stage called "Deep Enrichment." By applying computer vision, geospatial intersection analysis, and environmental modeling, we transform basic coordinates and imagery into complex economic and ecological metrics. This enables API consumers to ingest "computed intelligence" without needing to process raw media files manually.

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1. Dataset: Hazards & Risks (hazards_v1)

Source: Trouble Map, Research Module

This layer converts visual evidence of environmental threats into actionable remediation data.

• Volume & Morphology: The system estimates waste volume in cubic meters ($m^3$) and identifies composition (e.g., percentage of plastics, tires, organic, or hazardous materials).

• Remediation Costing: Automated calculation of cleanup costs based on identified volume, material type, and regional labor/machinery rates.

• Environmental Risk Scoring:

  • Fire Risk: Probability of spontaneous combustion based on material type and current meteorological conditions (temperature, humidity, wind).

  • Leachate Threat: Analysis of contamination risks to water bodies (rivers, lakes, aquifers) based on proximity to GIS-mapped hydrological vectors.

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2. Dataset: Biodiversity & Forest (bio_v1)

Source: Planting Events, Satellite Monitor, Citizen Science

This layer transforms planting reports into verifiable climate assets and biological records.

• Carbon Stock Modeling: Estimation of carbon sequestration potential (kg $CO_2$/year) using allometric equations specific to the identified tree species, age, and climate zone.

• Botanical Verification: AI-driven species identification (Scientific Latin names) with a confidence score to ensure adherence to biodiversity standards.

• Health & Vitality Metrics:

  • Vegetation Indices: Integration of multi-spectral data (NDVI, SAVI, NDRE) to track foliage health over time.

  • Survival Forecasting: Predictive modeling of sapling survival rates for the upcoming 12 months based on forecasted drought anomalies or soil stress factors.

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3. Dataset: Urban Infrastructure (urban_v1)

Source: Urban Repair Events, City Check Tasks

This layer converts urban damage reports into prioritized maintenance and accessibility insights.

• Severity & Criticality: An automated score (1–10) determining repair urgency based on the damage type's impact on public safety and infrastructure longevity.

• Material & Texture Identification: Classification of surface materials (Asphalt, Concrete, Paving Stones) to assist in logistical planning for repairs.

• Accessibility Impact: Automated analysis of whether a defect (e.g., a pothole or blocked ramp) violates accessibility standards for wheelchairs or strollers.

• Social Sentiment: Aggregated community urgency score based on local engagement, comments, and upvotes within the platform.

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4. Data Quality & Trust Layer (Meta-Intelligence)

To ensure high reliability for automated trading agents and institutional users, every data point in the Earth Data API is enriched with Quality Assurance (QA) metrics:

• Trust Score: (0.0 – 1.0) A composite reliability rating provided by the Truth Engine.

• Data Freshness: The time elapsed since the last ground-truth update (Scout visit) or satellite pass.

• Source Lineage: Clear identification of the origin type (USER_REPORT, CERTIFIED_SCOUT, IOT_SENSOR, or SATELLITE_GEE).

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Integration Value

These protocols transform Envorum from a simple map of photos into a computable knowledge graph of the physical world. This enriched intelligence is ready for direct ingestion by Smart City Digital Twins, Insurance Risk Models, and ESG Audit platforms.