In development

TopoVision

Extracting topographical data from maps using computer vision

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Problem & Impact

Current architectural and civil engineering workflows face a significant bottleneck: converting 2D topographical maps (contour lines) into usable 3D digital assets. This process often requires tedious manual data entry or fragile procedural generation tools, slowing down site analysis and environmental simulations. TopoVision solves this by automating the translation of static height curve maps into dynamic 3D meshes. For Architects/Engineers: drastic reduction in hours spent digitizing land plots. For ReGen Villages: accelerates the work of designing self-sustaining communities by allowing rapid analysis of different land plots for flood risks, solar exposure, and terraforming needs.

Tech Stack

Python
PyTorch

Key Features

  • Computer Vision Pipeline: Utilizes deep learning (PyTorch) to interpret height curves and visual features from standard image inputs (JPG/PNG).
  • Synthetic Training Pipeline: Overcomes data scarcity by leveraging a custom-built synthetic map generator to train the model on diverse topographical scenarios.
  • 2D-to-3D Reconstruction: Directly outputs a 3D elevation model (Digital Elevation Model or Mesh) from a single 2D input image.

Timeline

Start Date

November 2025

Current Phase

Development and research phase

Upcoming Milestones

  • Complete synthetic training pipeline
  • Optimize 2D-to-3D reconstruction algorithm
  • Project completion by Jan 31, 2025

Affiliations

ReGen Villages

Maintenance & Deployment

Research project with active development by KTH AI Society team in collaboration with ReGen Villages

Contributors

MK

Mattias Kvist

Research Lead

EL

Erik Lidman Hillbom

Researcher

ED

Edoardo de Cal

Researcher

AD

Annysia Dupaya

Researcher