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Replicable AI for Microplanning (Ramp) Bootstrap Model

The Replicable AI for Microplanning (Ramp) deep learning model is a semantic segmentation one which detects buildings from satellite imagery and delineates the footprints in low-and-middle-income countries (LMICs) using satellite imagery and enables in-country users to build their own deep learning models for their regions of interest. The architecture and approach were inspired by the Eff-UNet model outlined in this CVPR 2020 Paper.


MLHub model id: model_ramp_baseline_v1. Browse on Radiant MLHub.

ML Model Documentation

Please review the model architecture, license, applicable spatial and temporal extents and other details in the model documentation.

System Requirements

Hardware Requirements

Inferencing Training
RAM 4 GB RAM View Ramp model card
NVIDIA GPU optional required

Get Started With Inferencing

First clone this Git repository. Please note: this repository uses Git Large File Support (LFS) to include the model checkpoint file. Either install git lfs support for your git client, use the official Mac or Windows GitHub client to clone this repository.

git clone
cd model_ramp_baseline/

After cloning the model repository, you can use the Docker Compose runtime files as described below.

Please note: these command-line examples were tested on Linux and MacOS. Windows and WSL users may need to substitute appropriate commands, especially for setting environment variables.

Pull or Build the Docker Image

Pull pre-built image from Docker Hub (recommended):

# cpu
docker pull
# optional, for NVIDIA gpu
docker pull

Or build image from source:

# cpu
docker build -t radiantearth/model_ramp_baseline:1 -f Dockerfile_cpu .
# for NVIDIA gpu
docker build -t radiantearth/model_ramp_baseline:1-gpu -f Dockerfile_gpu .

Run Model to Generate New Inferences

  1. Prepare your input and output data folders. The data/ folder in this repository contains some placeholder files to guide you.

    • The data/ folder must contain:
      • input/chips imagery chips for inferencing. For example, Maxar ODP imagery
        • File name: chip_id.tif for example: 0fec2d30-882a-4d1d-a7af-89dac0198327.tif.
        • File Format: GeoTIFF, 256x256
        • Coordinate Reference System: WGS84, EPSG:4326
        • Bands: 3 bands per file:
          • Band 1 Type=Byte, ColorInterp=Red
          • Band 2 Type=Byte, ColorInterp=Green
          • Band 3 Type=Byte, ColorInterp=Blue
      • /input/ the model checkpoint folder in tensorflow format. Please note: the model checkpoint is included in this repository.
    • The output/ folder is where the model will write inferencing results.
  2. Set INPUT_DATA and OUTPUT_DATA environment variables corresponding with your input and output folders. These commands will vary depending on operating system and command-line shell:

    # change paths to your actual input and output folders
    export INPUT_DATA="/home/my_user/model_ramp_baseline/data/input/"
    export OUTPUT_DATA="/home/my_user/model_ramp_baseline/data/output/"
  3. Run the appropriate Docker Compose command for your system:

    Use either docker compose or docker-compose depending on your system.

    # cpu
    docker compose up model_ramp_baseline_v1_cpu
    # NVIDIA gpu driver
    docker compose up model_ramp_baseline_v1_gpu
  4. Wait for the docker compose to finish running, then inspect the OUTPUT_DATA folder for results.

Understanding Output Data

Please review the model output format and other technical details in the model documentation.