https://github.com/radiantearth/model_ramp_baseline
Replicable AI for Microplanning (Ramp) Bootstrap Model
https://github.com/radiantearth/model_ramp_baseline
earth-observation machine-learning tensorflow
Last synced: about 1 year ago
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Replicable AI for Microplanning (Ramp) Bootstrap Model
- Host: GitHub
- URL: https://github.com/radiantearth/model_ramp_baseline
- Owner: radiantearth
- License: other
- Created: 2022-08-25T21:49:03.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2023-09-25T09:34:00.000Z (almost 3 years ago)
- Last Synced: 2025-03-26T06:23:59.146Z (over 1 year ago)
- Topics: earth-observation, machine-learning, tensorflow
- Language: Jupyter Notebook
- Homepage: https://mlhub.earth/model/model_ramp_baseline_v1
- Size: 18.5 MB
- Stars: 7
- Watchers: 3
- Forks: 7
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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](https://openaccess.thecvf.com/content_CVPRW_2020/papers/w22/Baheti_Eff-UNet_A_Novel_Architecture_for_Semantic_Segmentation_in_Unstructured_Environment_CVPRW_2020_paper.pdf).
MLHub model id: `model_ramp_baseline_v1`. Browse on [Radiant MLHub](https://mlhub.earth/model/model_ramp_baseline_v1).
## ML Model Documentation
Please review the model architecture, license, applicable spatial and temporal extents
and other details in the [model documentation](/docs/index.md).
## System Requirements
* Git client
* [Docker](https://www.docker.com/) with
[Compose](https://docs.docker.com/compose/) v1.28 or newer.
## Hardware Requirements
| |Inferencing|Training|
|------------|-----------|--------|
| RAM | 4 GB RAM | [View Ramp model card](https://rampml.global/ramp-model-card/) |
| NVIDIA GPU | optional | [required](https://rampml.global/ramp-model-card/) |
## Get Started With Inferencing
First clone this Git repository. Please note: this repository uses
[Git Large File Support (LFS)](https://git-lfs.github.com/) 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.
```bash
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 or Build the Docker Image
Pull pre-built image from Docker Hub (recommended):
```bash
# 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:
```bash
# 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](https://rampml.global/ramp-faqs/)
* 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/checkpoint.tf` 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:
```bash
# 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.
```bash
# 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](/docs/index.md).