Technical Project Details.
Public-safe technical summaries focused on methods, measurable outcomes and tools. Client identities, exact project locations and confidential deliverables remain omitted.
Multispectral Land-Cover Classification
Semantic segmentation of complex inland and coastal land-cover using RGB + NIR imagery.
- Built a custom U-Net semantic-segmentation workflow with a ResNet-34 backbone.
- Worked with processed RGB + NIR JP2 imagery where original reflectance values were unavailable.
- Defined nine target classes and prepared a grid-based training/sample workflow.
- Reported overall F1 above 94% in the supplied assessment.
Terrain-Based Gully Detection
Large-scale terrain ML workflow for automated gully detection from very high-resolution DEM derivatives.
- Modeled approximately 6.188 million samples using 13 terrain-derived features.
- Reused cleaned historical samples to reduce repetitive annotation effort.
- Used independent train/test splitting and 5-fold stratified cross-validation.
- Reported mean CV AUC 0.9652, test AUC 0.9650 and F1 0.8224.
- Validated temporal generalization on a later DEM without retraining.
OCR Model Deployment & Workflow Automation
Production-oriented OCR deployment integrated with GIS and automated business workflows.
- Integrated a fine-tuned OCR model behind FastAPI inference services.
- Designed API endpoints for text extraction from field-captured imagery.
- Connected GIS, API and workflow automation to update feature attributes and log outputs.
- Packaged OCR capabilities into GIS tooling to reduce manual transcription.
Vegetation Mapping from Drone Imagery
Object-based vegetation mapping using multispectral UAV imagery and vegetation indices.
- Processed RGB, NIR and Red Edge drone imagery.
- Computed NDVI, NDRE and MSAVI to support segmentation and vegetation discrimination.
- Performed object-based segmentation/classification in eCognition.
- Integrated domain-expert input to refine the classification schema.
Wetland Classification using SAR
L-band SAR preprocessing, decomposition and classification workflow for wetland mapping.
- Performed radiometric calibration, speckle filtering and terrain correction.
- Generated Freeman-Durden and Touzi decomposition layers.
- Combined SAR outputs with terrain-derived ancillary information.
- Focused classification refinement on forested wetlands, emergent wetlands and open water.
Vegetation Health Monitoring
Time-series vegetation-condition monitoring using high-resolution imagery and MSAVI.
- Computed MSAVI and performed multi-date trend analysis.
- Produced zone-level statistical summaries for vegetation condition.
- Integrated medium-resolution satellite indices for longer-term context.
- Delivered analysis-ready summaries and geospatial layers.
Historical Aerial Image Classification
Proof-of-concept deep-learning classification for archival black-and-white aerial imagery.
- Applied contrast correction, denoising and band-stacking preprocessing.
- Converted single-band grayscale inputs into model-ready multidimensional representations.
- Used pretrained CNN backbones for classification.
- Reported greater than 90% proof-of-concept classification accuracy.
Automated DEM Download & Mosaicking
Python automation for bulk DEM acquisition and mosaicking.
- Automated tile discovery, download and mosaicking from public elevation-data services.
- Added parameterized area selection for reusable workflows.
- Reduced manual download effort by an estimated 70–80%.