Data

Tools

Indexes

Applications

Experiments

Open Source Science

orthoseg

OrthoSeg makes it easy to train neural networks to segment orthophotos.

https://github.com/orthoseg/orthoseg

Science Score: 67.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 1 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: zenodo.org
  • ○
    Committers with academic emails
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (10.8%) to scientific vocabulary

Keywords

aerial-imagery convolutional-neural-networks deep-learning drone geoai image-classification image-segmentation keras-tensorflow machine-learning neural-network python remote-sensing satellite-imagery segment-orthophotos segmentation tensorflow wms

Keywords from Contributors

mesh energy-system-model large-files geospatial-processing gdal geopackage geopandas geoprocessing geospatial-data interpretability
Last synced: 6 months ago · JSON representation ·

Repository

OrthoSeg makes it easy to train neural networks to segment orthophotos.

Basic Info
Statistics
  • Stars: 35
  • Watchers: 4
  • Forks: 5
  • Open Issues: 20
  • Releases: 16
Topics
aerial-imagery convolutional-neural-networks deep-learning drone geoai image-classification image-segmentation keras-tensorflow machine-learning neural-network python remote-sensing satellite-imagery segment-orthophotos segmentation tensorflow wms
Created over 7 years ago · Last pushed 6 months ago
Metadata Files
Readme Changelog License Citation

README.md

Orthophoto segmentation

Actions Status Coverage Status PyPI version DOI

A python package that makes it (relatively) easy to segment orthophotos. Any type of georeferenced images should work, e.g. satellite, aerial or drone images, (historical) maps, hillshades,...

No programming is needed, everything is managed via configuration files.

The typical steps: 1. create a training dataset for a topic of your choice, e.g. in QGIS 2. train a neural network to segment orthophotos 3. run the segmentation on a larger area + vectorize the result 4. apply some basic postprocessing like dissolve, simplify,...

Only open source software is needed, eg. QGIS and tensorflow.

Installation and usage instructions can be found in the orthoseg docs

This is an example of how the output of a tree detection on aerial images can look:

Result of a tree detection on aerial images

Owner

  • Name: orthoseg
  • Login: orthoseg
  • Kind: organization

Citation (CITATION.cff) 

cff-version: 1.2.0
message: If you use this software, please cite it as below.
type: software
title: orthoseg
version: 0.6.0
date-released: 2024-07-26
doi: 10.5281/zenodo.12938241
abstract: OrthoSeg makes it easy to train neural networks to segment orthophotos.
url: https://github.com/orthoseg/orthoseg
repository-code: https://github.com/orthoseg/orthoseg
license: GPL-3.0
authors:
  - given-names: Pieter
    family-names: Roggemans
    orcid: https://orcid.org/0009-0009-2046-3284

keywords:
  - orthoseg
  - geospatial
  - image-segmentation

GitHub Events

Total
  • Create event: 12
  • Release event: 2
  • Issues event: 9
  • Watch event: 5
  • Delete event: 6
  • Issue comment event: 42
  • Push event: 58
  • Pull request review comment event: 2
  • Gollum event: 3
  • Pull request review event: 6
  • Pull request event: 112
  • Fork event: 1
Last Year
  • Create event: 12
  • Release event: 2
  • Issues event: 9
  • Watch event: 5
  • Delete event: 6
  • Issue comment event: 42
  • Push event: 58
  • Pull request review comment event: 2
  • Gollum event: 3
  • Pull request review event: 6
  • Pull request event: 112
  • Fork event: 1

Committers

Last synced: 9 months ago

All Time
  • Total Commits: 827
  • Total Committers: 6
  • Avg Commits per committer: 137.833
  • Development Distribution Score (DDS): 0.036
Past Year
  • Commits: 77
  • Committers: 4
  • Avg Commits per committer: 19.25
  • Development Distribution Score (DDS): 0.26
Top Committers
Name Email Commits
theroggy p****s@g****m 797
dependabot[bot] 4****] 18
Kris Van Wayenberge 3****V 8
Kris Van Wayenberge 3****y 2
pre-commit-ci[bot] 6****] 1
joebro j****x@l****e 1
Committer Domains (Top 20 + Academic)
lv.vlaanderen.be: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 55
  • Total pull requests: 229
  • Average time to close issues: 3 months
  • Average time to close pull requests: 7 days
  • Total issue authors: 4
  • Total pull request authors: 4
  • Average comments per issue: 0.16
  • Average comments per pull request: 0.57
  • Merged pull requests: 210
  • Bot issues: 0
  • Bot pull requests: 22
Past Year
  • Issues: 10
  • Pull requests: 76
  • Average time to close issues: 20 days
  • Average time to close pull requests: 10 days
  • Issue authors: 1
  • Pull request authors: 4
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.74
  • Merged pull requests: 63
  • Bot issues: 0
  • Bot pull requests: 14
View more stats
Top Authors
Issue Authors
  • theroggy (52)
  • jp-um (1)
  • mark-borg (1)
  • TashinAhmed (1)
Pull Request Authors
  • theroggy (250)
  • dependabot[bot] (37)
  • KriWay-LV (19)
  • pre-commit-ci[bot] (2)
  • KriWay (1)
Top Labels
Issue Labels
bug (4) enhancement (2) upstream issue (1) good first issue (1)
Pull Request Labels
dependencies (37) github_actions (27)

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 273 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 1
  • Total versions: 56
  • Total maintainers: 2
pypi.org: orthoseg

Package to make it easier to segment orthophotos.

  • Versions: 56
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 273 Last month
Rankings
Dependent packages count: 10.1%
Stargazers count: 14.2%
Average: 15.8%
Downloads: 16.1%
Forks count: 16.8%
Dependent repos count: 21.6%
Maintainers (2)
JoeriB theroggy
Last synced: 6 months ago

Dependencies

.github/workflows/release_tag_to_pypi.yml actions
  • actions/checkout v3 composite
  • actions/setup-python v3 composite
  • pypa/gh-action-pypi-publish 27b31702a0e7fc50959f5ad993c78deac1bdfc29 composite
.github/workflows/tests.yml actions
  • actions/checkout v2 composite
  • codecov/codecov-action v2 composite
  • conda-incubator/setup-miniconda v2 composite
pyproject.toml pypi
setup.py pypi
  • gdown *
  • geofileops >=0.6,<0.8
  • geopandas >=0.12
  • owslib *
  • pillow *
  • pycron *
  • pygeoops >=0.2,<0.3
  • rasterio *
  • segmentation-models >=1.0,<1.1
  • shapely >=2
  • simplification *
  • tensorflow >=2.7,<2.11