building-footprint-segmentation
Building footprint segmentation from satellite and aerial imagery
https://github.com/fuzailpalnak/building-footprint-segmentation
Science Score: 44.0%
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
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○Academic publication links
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○Committers with academic emails
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (8.7%) to scientific vocabulary
Keywords
Repository
Building footprint segmentation from satellite and aerial imagery
Basic Info
- Host: GitHub
- Owner: fuzailpalnak
- License: apache-2.0
- Language: Python
- Default Branch: main
- Homepage: https://fuzailpalnak-buildingextraction-appbuilding-extraction-s-ov1rp9.streamlitapp.com/
- Size: 548 KB
Statistics
- Stars: 157
- Watchers: 2
- Forks: 33
- Open Issues: 2
- Releases: 7
Topics
Metadata Files
README.md
Building Footprint Segmentation
Library to train building footprint on satellite and aerial imagery.

Installation
pip install building-footprint-segmentation
Dataset
Training
Train With Config , Use config template for generating training config
Visualize Training
Test images at end of every epoch
- Follow Example
Visualizing on Tensorboard
```python
from buildingfootprintsegmentation.helpers.callbacks import CallbackList, TensorBoardCallback
wheretologthecallback = r"pathtolog_callback"
callbacks = CallbackList()
Ouptut from all the callbacks caller will be stored at the path specified in log_dir
callbacks.append(TensorBoardCallback(wheretologthecallback))
```
To view Tensorboard dash board
tensorboard --logdir="path_to_log_callback"
Defining Custom Callback
```python from buildingfootprintsegmentation.helpers.callbacks import CallbackList, Callback
class CustomCallback(Callback): def init(self, logdir): super().init(logdir)
wheretologthecallback = r"pathtolog_callback"
callbacks = CallbackList()
Ouptut from all the callbacks caller will be stored at the path specified in log_dir
callbacks.append(CustomCallback(wheretologthecallback)) ```
Split the images in smaller sample
```python import glob import os
from image_fragment.fragment import ImageFragment
FOR .jpg, .png, .jpeg
from imageio import imread, imsave
FOR .tiff
from tifffile import imread, imsave
ORIGINALDIMOFIMAGE = (1500, 1500, 3) CROPTO_DIM = (384, 384, 3)
imagefragment = ImageFragment.imagefragment3d( fragmentsize=(384, 384, 3), orgsize=ORIGINALDIMOFIMAGE )
IMAGEDIR = r"pth\to\input\dir" SAVEDIR = r"pth\to\save\dir"
for file in glob.glob( os.path.join(IMAGEDIR, "*") ): image = imread(file) for i, fragment in enumerate(imagefragment): # GET DATA THAT BELONGS TO THE FRAGMENT fragmentedimage = fragment.getfragment_data(image)
imsave(
os.path.join(
SAVE_DIR,
f"{i}_{os.path.basename(file)}",
),
fragmented_image,
)
```
Inference
Segmentation for building footprint
- [x] binary
- [ ] building with boundary (multi class segmentation)
Weight File
Refer gtkit for commonly used utility task when working with Geotiff
Owner
- Name: Fuzail Palnak
- Login: fuzailpalnak
- Kind: user
- Location: Graz, Austria
- Company: Joanneum Research
- Website: https://fuzailpalnak.github.io
- Repositories: 12
- Profile: https://github.com/fuzailpalnak
In a quest to improve my machine learning and computer vision skills
Citation (CITATION.cff)
cff-version: 1.2.0
message: "Please cite this repository If you found it helpful in your reserach."
preferred-citation:
type: generic
authors:
- family-names: "Palnak"
given-names: "Fuzail A."
title: "building-footprint-segmentation"
url: "https://github.com/fuzailpalnak/building-footprint-segmentation"
GitHub Events
Total
- Watch event: 23
- Fork event: 3
Last Year
- Watch event: 23
- Fork event: 3
Committers
Last synced: 9 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Fuzail palnak | f****k@g****m | 67 |
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 19
- Total pull requests: 31
- Average time to close issues: 2 months
- Average time to close pull requests: less than a minute
- Total issue authors: 14
- Total pull request authors: 2
- Average comments per issue: 6.26
- Average comments per pull request: 0.0
- Merged pull requests: 30
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 1
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 1
- Average comments per issue: 0
- Average comments per pull request: 0.0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- santhi-2020 (3)
- gmommi (2)
- JuliaWasala (2)
- teresalisanti (2)
- kavin160100 (1)
- trecuu (1)
- fitzgeraldja (1)
- hieule88 (1)
- Tclack88 (1)
- Finn-Neo (1)
- raialvaro (1)
- fbernardini (1)
- Juldeng (1)
- Plan-T42 (1)
Pull Request Authors
- fuzailpalnak (30)
- AndyWarrior123 (2)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 2
-
Total downloads:
- pypi 49 last-month
-
Total dependent packages: 0
(may contain duplicates) -
Total dependent repositories: 1
(may contain duplicates) - Total versions: 12
- Total maintainers: 1
proxy.golang.org: github.com/fuzailpalnak/building-footprint-segmentation
- Documentation: https://pkg.go.dev/github.com/fuzailpalnak/building-footprint-segmentation#section-documentation
- License: apache-2.0
-
Latest release: v0.2.4
published over 3 years ago
Rankings
pypi.org: building-footprint-segmentation
Building footprint segmentation from satellite and aerial imagery
- Homepage: https://github.com/fuzailpalnak/building-footprint-segmentation
- Documentation: https://building-footprint-segmentation.readthedocs.io/
- License: MIT License
-
Latest release: 0.2.4
published over 3 years ago
Rankings
Maintainers (1)
Dependencies
- PyYAML ==5.3.1
- albumentations ==0.5.0
- numpy ==1.19.2
- opencv_python_headless ==4.4.0.44
- py_oneliner ==0.0.1
- scikit_learn ==0.23.2
- torch *
- torchvision *
- tqdm ==4.51.0
- actions/checkout v2 composite
- actions/setup-python v2 composite
- pypa/gh-action-pypi-publish 27b31702a0e7fc50959f5ad993c78deac1bdfc29 composite