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.
Please review the model architecture, license, applicable spatial and temporal extents and other details in the model documentation.
Inferencing | Training | |
---|---|---|
RAM | 4 GB RAM | View Ramp model card |
NVIDIA GPU | optional | required |
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 https://github.com/radiantearth/model_ramp_baseline.git
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 pre-built image from Docker Hub (recommended):
# cpu
docker pull docker.io/radiantearth/model_ramp_baseline:1
# optional, for NVIDIA gpu
docker pull docker.io/radiantearth/model_ramp_baseline:1-gpu
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 .
Prepare your input and output data folders. The data/
folder in this repository
contains some placeholder files to guide you.
data/
folder must contain:
input/chips
imagery chips for inferencing.
For example, Maxar ODP imagery
chip_id.tif
for example:
0fec2d30-882a-4d1d-a7af-89dac0198327.tif
./input/checkpoint.tf
the model checkpoint folder in tensorflow format.
Please note: the model checkpoint is included in this repository.output/
folder is where the model will write inferencing results.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/"
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
Wait for the docker compose
to finish running, then inspect the
OUTPUT_DATA
folder for results.
Please review the model output format and other technical details in the model documentation.