[Data Source Proposal]: Farm dams of Western Australia (DPIRD-083)
まだ誰も着手していません。
評価
調査の方向性
Start with the proposal’s dataset catalogue page and the related issue #580, which contains the imagery review and full list of suspected building footprints. Assess whether the proposed dam polygons can be incorporated into the water theme and the existing building filter, including licensing, data quality, and update considerations. Done means a decision on the proposal and, if accepted, an agreed integration approach.
索引モデルが issue の本文から書いたものです。
説明
Dataset name
Farm dams of Western Australia (DPIRD-083)
Source URL
https://catalogue.data.wa.gov.au/dataset/farm-dams-of-the-south-west-agricultural-region-of-wa
Your relationship to this data
I publish or maintain it
Overture theme
Base, Buildings
Geographic coverage
Western Australia, concentrated in the South West agricultural region (99% of features are south of 29°S)
What the data would add
175,143 farm dam polygons, which would help Overture in two places:
- Base / water. Farm dams in WA are almost entirely missing from the water theme. Of 15,282 buildings found sitting on DPIRD dams, only 289 (1.9%) have their centre inside any polygon in
base/water(release 2026-09-23.1). - Buildings. As an input to the existing filter that excludes ML buildings crossing water. About 15,000 Microsoft ML footprints in WA are farm dams mistaken for buildings. A 100-hit imagery review found 99 not buildings. Details and the full list: #580
The dams are small: median 0.24 ha, and 97.6% under 1 ha. Most are too small for 30 m products like DEA Waterbodies to capture, which is the gap this fills.
License
CC-BY-4.0
Other license details
Requested attribution: © State of Western Australia, Department of Primary Industries and Regional Development.
Is this data already in OpenStreetMap?
No
Format, updates, and other details
Format: File Geodatabase, GeoPackage, GeoJSON and Shapefile downloads (SLIP account login), plus anonymous ArcGIS REST, WMS and WFS services, all linked from the catalogue page. Polygons with an objectid and no other attributes.
Updated: As required
Collection method: Deep-learning image segmentation of Vivid 2.0 satellite imagery, mostly captured 2018–2019, with each feature filtered by a second deep-learning classifier. Sites were preselected from the earlier South West farm dams dataset, DEA Waterbodies and Landgate's Medium Scale Topo Water polygons.
Known quality issues: within the South West agricultural region, the metadata estimates about 24,000 farm dams are missing and about 1,800 features are not farm dams. A subset was manually reviewed in QGIS. Some dams have been filled in or built over since the 2018–19 imagery.
Before submitting
- I searched existing proposals and didn't find this dataset.
- I understand that submitting a proposal doesn't guarantee Overture will add the data.
- 主要言語
- Python
- スター
- 1.2k
- フォーク
- 48
- 平均マージ
- 56分
- マージ済み PR（30日）
- 2
環境構築
- Dockerfile・Docker Compose ファイルなし
- プルリクエストのテンプレートなし
- コントリビューションガイドを読む
はじめの一歩
- issue を最後まで読み、次にプロジェクトのコントリビューションガイドを読みます。
- 着手することを issue にコメントします — 二人が同じ作業をするのを防げます。
- リポジトリをフォークし、ブランチを切って変更します。
- issue 番号を参照したプルリクエストを送ります。
OvertureMaps/data のほかの issue
-
external
難易度 2/5 1〜3時間 初心者へのやさしさ 68/100
OvertureMaps/data#584 ·
-
external
難易度 2/5 1〜3時間 初心者へのやさしさ 70/100
OvertureMaps/data#509 ·
-
Places (FI): ~90 duplicate, misplaced, non-pharmacy or closed features in the pharmacy categoryオープンexternal
難易度 4/5 3〜5日 初心者へのやさしさ 49/100
OvertureMaps/data#585 ·
-
external
難易度 4/5 3〜5日 初心者へのやさしさ 22/100
OvertureMaps/data#583 ·
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bug external
難易度 4/5 3〜5日 初心者へのやさしさ 48/100
OvertureMaps/data#580 · コメント 1 件 ·
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