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https://github.com/diovani-dupont/solarpaneldetection
The following code is a script developed for a challenging competition in Solafune's Data Science area, focused on detecting solar panels in satellite images.
https://github.com/diovani-dupont/solarpaneldetection
Last synced: about 2 months ago
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The following code is a script developed for a challenging competition in Solafune's Data Science area, focused on detecting solar panels in satellite images.
- Host: GitHub
- URL: https://github.com/diovani-dupont/solarpaneldetection
- Owner: diovani-dupont
- License: other
- Created: 2023-11-12T02:39:24.000Z (about 1 year ago)
- Default Branch: master
- Last Pushed: 2023-11-12T02:52:48.000Z (about 1 year ago)
- Last Synced: 2023-11-12T03:26:28.352Z (about 1 year ago)
- Language: Jupyter Notebook
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
SolarPanelDetection
==============================
The following code is a script developed for a challenging competition in Solafune's Data Science area, focused on detecting solar panels in satellite images.
This competition aims to develop an advanced technique for segmenting solar panels in optical satellite images. Accurate detection of solar panels from satellite images is crucial for energy supply planning, infrastructure optimization and disaster prediction. The challenge involves using Sentinel-2 satellite images, which have relatively low resolution, to identify and segment solar panels.
Project Organization
------------
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to download or generate data
│ │ └── make_dataset.py
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ ├── predict_model.py
│ │ └── train_model.py
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
│
└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io
--------
Project based on the cookiecutter data science project template. #cookiecutterdatascience