Awesome

Semantics from Space: Satellite-Guided Thermal Semantic Segmentation Annotation for Aerial Field Robots

Welcome to the Semantics from Space repository! This project introduces a novel method for automatically generating semantic segmentation annotations for thermal imagery obtained from aerial vehicles. Our method leverages satellite-derived data products in conjunction with onboard global positioning and attitude estimates. This allows for precise and rapid annotation of thermal and non-thermal imagery at a massively-parallelizable scale. Please see our arxiv preprint for more details:

[1] Lee, C., Soedarmadji, S., Anderson, M., Clark, A. J., & Chung, S. J. (2024). Semantics from Space: Satellite-Guided Thermal Semantic Segmentation Annotation for Aerial Field Robots. arXiv preprint arXiv:2403.14056.

Key Features:

🤖 Automatic generation of semantic segmentation annotations for thermal imagery.
🛰️ Utilization of satellite-derived data products and onboard sensor data.
🔄 Incorporation of thermal-conditioned refinement step with visual foundation models.
📈 Achieves 98.5% performance compared to costly high-resolution options.
🚀 Demonstrates 70-160% improvement over existing zero-shot segmentation methods.

Feel free to explore the codebase and contribute to further advancements in thermal semantic perception algorithms!

flowchart

Environment Setup

Setup anaconda environment.

# Create a new environment (using whatever environment manager you prefer)
conda create --name autoseg python=3.11.3
conda activate autoseg

# Install dependencies (using `python -m pip` to guard against using incorrect pip version)
python -m pip install --requirement requirements.txt
python -m pip install --no-build-isolation git+https://github.com/lucasb-eyer/pydensecrf.git

Thermal Dataset

If you want to use the same data we use in our paper, please get the dataset from here: https://github.com/aerorobotics/caltech-aerial-rgbt-dataset. Follow the instructions in that repository to download and extract the thermal data and the aligned.csv files containing GPS, pose, and time-sync information.

Satellite-Derived Data Download and Preprocessing

Scripts and notebooks like dynamicworld_export.py and those in folders earth_engine_download and microsoft_planetarycomputer_download are used to download geospatial data like landcover labels and high-resolution orthoimagery from the Google Earth Engine and Microsoft Planetary Computer.

Download DynamicWorld Land Use Land Cover (LULC) sets

Run bash/dynamicworld_export.sh to export data to Google Cloud Storage. Change dynamicworld_export.py to use Google Drive if desired but this is not recommended.
Move downloaded tiles into /data/microsoft_planetery_computer/dynamicworld/PLACE/tiles/ and /data/chesapeake_bay_lulc/dynamicworld/PLACE/tiles/ or into your desired storage locations.

Notes:

Use the tag --dryrun to save a local preview of the LULC mosaic before committing to the full export.
Modify the --start-date and --end-date as needed. This range should just encompass the date the data trajectory was taken, but enlarged such that a full mosaic can be rendered (missing data may be present in some DynamicWorld tile rasters).

Download NAIP/Planet/DSM/DEM tiles

Use scripts in microsoft_planetarycomputer_download to download DSM, DEM, NAIP from 2010-2021.
Use EarthExplorer to download NAIP after 2021.
Place all downloaded tiles in their respective folders (use custom folder paths if desired).
Repeat all this for Chesapeake Bay Program tiles (or any other LULC dataset with labels and high-resolution imagery) if you're interested in tuning a Dense CRF for LULC refinement.

Paths to directory on lambda:

NAIP path: /data/microsoft_planetary_computer/naip/PLACE/tiles
Planet path: /data/microsoft_planetary_computer/planet/PLACE/[visual / 4band]
Digital Surface Map (DSM) path: /data/microsoft_planetary_computer/dsm/PLACE/tiles
Digital Elevation Map (DEM 10 meters) path: /data/microsoft_planetary_computer/dem/PLACE/tiles
Digital Elevation Map (DEM 1 meter) path: /data/microsoft_planetary_computer/dem_1m/PLACE/tiles

Dataset	NAIP (0.6/1.0m) visual quality	DSM (2m) Available	DEM (10m) Available	DEM (1m) Available
Duck	Raster borders are obvious; difference in water reflectance	Yes	Yes	No
Kentucky River	Fine	Yes	Yes	Possibly (but not downloaded)
Colorado River	Fine	No	Yes	Yes (looks odd)
Castaic Lake	Fine	Yes	Yes	Yes

Create and preprocess tiles into mosaics

Note: please update folder paths as needed. The example paths are customized for our own work.

Ensure DynamicWorld and other data are downloaded and in their respective folders.
Create mosaics from the downloaded raster tiles using bash/merge_original_tiles.sh /data/microsoft_planetary_computer and bash/merge_original_tiles.sh /data/chesapeake_bay_lulc.
Combine planet RGB (3 band) with NIR from 4 band surface reflectance data using bash bash/stack_planet_visual_nir.sh. Note: this stores directly into the mosaic directories. Note: this step is necessary because NIR and RGB from Planet are provided separately.
Preprocess (reproject, resample, crop to bounds) using bash/batch_preprocess.sh /data/microsoft_planetary_computer and bash/batch_preprocess.sh /data/chesapeake_bay_lulc.

LULC refinement via Conditional Random Fields

First, change to the appropriate directory.

cd lulc_refinement

Convert DynamicWorld and Chesapeake Bay LULC labels into a common set of labels (see table below) via bash bash/commonize_lulc_labels.sh. This is necessary if training/evaluating CRF refinement using the 1-meter resolution labels from Chesapeake Bay as ground truth. This step can be skipped if just doing CRF refinement without training but is still recommended to merge some similar labels.

New Index	Common Name	Dynamic World	Chesapeake Bay
0	Water	Water	Water
1	Trees	Trees	Tree Canopy<br/> Tree Canopy over Impervious Structures<br/> Tree Canopy over Other Impervious<br/> Tree Canopy over Impervious Roads
2	Low Brush	Grass<br/> Crops	Low Vegetation
3	Shrub and Scrub	Shrub and Scrub	Shrub and Scrub
4	Wetlands	Flooded Vegetation	Emergent Wetlands
5	Human-made Structures	Built Up Area	Impervious Structures<br/> Other Impervious<br/> Impervious Roads
6	Bare Ground	Bare Ground	Barren
7	N/A	Snow/Ice
8	N/A		Aberdeen Proving Grounds

To train, run

bash bash/train.sh

This will perform parameter optimization on the dense CRF using Optuna. See the bash script and accompanying code for more details on command line flags.

To run inference on aerial imagery, run

bash bash/refine.sh

or other similar scripts in the same directory following the pattern *refine*.sh.

Semantic Segmentation Annotation Generation

Coarse segmentation label rendering

This step will likely require tinkering with the code to update to your robotic platforms coordinate frame and sensor measurement conventions.

cd autosegment

Please see the python files autoseg_v2.py and gl_project.py. The code assumes particular coordinate frames, altitude measurements, etc... Please update the files accordingly.
Once updated, run:

bash bash/autoseg.sh

to generate the coarse segmentation labels.

Rendered label refinement

This should be much more straightforward compared to the previous step. First, switch to the appropriate directory:

cd autoseg_refinement

Use the Segment Anything repo to generate SAM segmentations. Combine the SAM segmentations (binary masks) into a single png with labels from 0-n for (n + 1) segmentation instances.
Use the files bash/refine_seg.sh and bash/refine_helper.sh as reference to run your Segment Anything-based refinement.

Issues and Contributing

If you find issues with this repo, or have code to contribute, please submit and issue and/or a PR above.