Dr. Mohd Radhie
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Geospatial AI • LiDAR • Remote Sensing • Natural Hazards

Turning earth data into decisions that matter.

I’m Dr. Mohd Radhie bin Mohd Salleh (UTM, Civil Engineering — Geoinformatics), working on LiDAR/DTM hydro-flattening, AI-driven hazard mapping (landslide & flood), dashcam-based road quality intelligence, and real-time monitoring dashboards.

Featured projects
UTM • FKA Workshops & training Industry collaboration Applied research

About

I build end-to-end geospatial systems: from data acquisition (LiDAR/UAV/satellite/dashcam), to AI modelling, to decision-ready web dashboards for agencies, industry and communities.

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Remote sensing & multi-source fusion

Satellite, UAV, LiDAR, bathymetry, and field data — fused into consistent, analysis-ready layers.

HSI / MSIGEE / PythonQA/QC

AI for geospatial applications

Object detection/segmentation and ML mapping for hazards, infrastructure, and environmental monitoring.

YOLO / U-NetXGBoost / KNNExplainable AI

Dashboards & operational systems

From sensors/APIs to interactive dashboards (maps + charts + alerts) for near-real-time decisions.

Leaflet / MapLibrePostGIS / GeoServerGAS / Web

Expertise

Core capabilities, tailored for applied research, consultancy, and training.

Geospatial AI & modelling

ML mapping for landslide/flood susceptibility, deep learning detection/segmentation, model validation, and deployment pipelines.

Training dataModel QAOperationalization

LiDAR / DTM hydro-flattening

Hydro-flattening workflows integrating LiDAR DTM and bathymetry; produces consistent terrain for hydrology and flood modelling.

DTMBathy fusionDrainage integrity

Natural hazards

Landslide activity classification using vegetation anomalies; flood risk indices and virtual-sensor style monitoring systems.

LandslideFloodRisk analytics

Street-level intelligence

Dashcam-based mapping for road surface quality and POI extraction; turning video into spatial insights.

Vision AIGeo-taggingQuality mapping

Featured projects

Click any project to open a detailed, interactive modal (objectives, methods, outcomes).

Research directions

A snapshot of topics I frequently publish, supervise, and build systems around.

Current themes (timeline)

Vegetation-based landslide activity classification

Vegetation Anomalies Indicator (VAI) + ensemble ML for Kundasang, Sabah.

AI-driven mapping from dashcam feeds

Road surface quality intelligence (detection → geo-referencing → dashboard).

Hydro-flattened DEM workflows

LiDAR + bathymetry integration for hydrology and flood modelling.

Carbon mapping & urban trees

Remote sensing methods for carbon density and monitoring.

Selected publication-ready topics

Landslide Susceptibility Mapping using Machine Learning (Kundasang, Sabah)
Benchmarking ML methods, feature engineering, validation, and interpretability.
Enhancing Landslide Activity Classification with Vegetation Anomalies Indicator (VAI)
Ensemble learning and vegetation anomalies for activity-level classification.
AI-driven mapping for road surface quality assessment (Malaysia context)
Dashcam video → detection → spatial dashboard for maintenance prioritization.
Remote sensing for water quality retrieval
Multi-source RS workflows for monitoring and decision support.
Research grants Postgrad supervision Industry R&D

Contact & collaboration

Tell me your problem statement. I’ll suggest a practical data + AI + dashboard pathway.

Demo form. Replace handler with your email/API/GAS endpoint later.

Fast links

Update these to your real contacts: Email: yourname@yourdomain.com

Consultancy Joint grant Workshop Supervision

Typical deliverables: maps, models, validation report, dashboard, API + database integration, and training materials.

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