Project Title Azure Workflow for KML Ingestion and High-Resolution Satellite Imagery Acquisition Project Objective Develop an automated cloud workflow that: - Detects uploaded .kml files and associated metadata (orchard name, tree variety, location, etc.) containing agricultural field / orchard boundaries - Extracts polygon geometry from each file - Automatically obtains very high resolution satellite imagery covering the polygon area - Stores imagery and metadata in structured Azure storage for downstream analysis IMPORTANT: This phase does NOT include tree detection, counting, or computer vision. This phase is strictly: - KML ingestion - AOI extraction - Satellite imagery retrieval (sub-meter resolution preferred) - Storage + metadata organization Target Platform Microsoft Azure (required) Functional Requirements 1. KML Ingestion Pipeline Monitor a designated cloud folder (OneDrive, SharePoint, or Azure Blob) Automatically trigger processing when a new .kml file is uploaded Support: single polygon multipolygon multiple features per file Extract: geometry coordinates bounding box area (hectares) centroid CRS handling (assume WGS84 but validate) 2. Area of Interest (AOI) Processing For each polygon: Generate buffered bounding box (configurable margin, e.g. 50–200 m) Prepare AOI query format compatible with imagery provider API Log AOI metadata 3. High-Resolution Satellite Imagery Retrieval System must be capable of retrieving imagery from an API provider. Developer should implement provider-agnostic architecture so imagery source can be changed later. Resolution target: Prefer ≤ 50 cm per pixel Must support configurable resolution target Acceptable imagery acquisition modes: archive imagery request tile mosaic download scene download covering AOI System must handle: API authentication query submission polling job status (if async) downloading imagery reprojection if needed clipping to AOI polygon (optional but preferred) 4. Imagery Storage Store outputs in Azure Blob Storage with structured hierarchy: /kml/ original files /imagery/raw/ /imagery/clipped/ /metadata/ json records Each processed AOI must produce: imagery file (GeoTIFF preferred) metadata JSON including: acquisition date provider spatial resolution CRS cloud cover (if available) AOI area bounding box processing timestamp 5. Processing Orchestration Implement automated cloud processing: Recommended architecture (open to alternatives): Azure Logic App OR Event Grid trigger Azure Function (Python preferred) Durable Functions if long-running Azure Blob Storage Key Vault for credentials Must support multiple concurrent KML uploads. 6. Logging and Error Handling System must: log processing steps log failed imagery queries flag AOIs where imagery unavailable provide retry mechanism