Data · collection · 2023
Drone Imagery Classification Training Dataset for Crop Types in Rwanda
Listed in NASA Earthdata CMR
RTI International (RTI) generated 2,611 labeled point locations representing 19 different land cover types, clustered in 5 distinct agroecological zones within Rwanda.
Description
These land cover types were reduced to three crop types (Banana, Maize, and Legume), two additional non-crop land cover types (Forest and Structure), and a catch-all Other land cover type to provide training/evaluation data for a crop classification model.
Each point is attributed with its latitude and longitude, the land cover type, and the degree of confidence the labeler had when classifying the point location. For each location there are also three corresponding image chips (4.5 m x 4.5 m in size) with the point id as part of the image name. Each image contains a P1, P2, or P3 designation in the name, indicating the time period.
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P1 corresponds to December 2018, P2 corresponds to January 2019, and P3 corresponds to February 2019. These data were used in the development of research documented in greater detail in “Deep Neural Networks and Transfer Learning for Food Crop Identification in UAV Images” (Chew et al., 2020).
Links
Get the data
- Dataset Detail and Download Page source.coop/rti/rwanda-crop-type ↗
landing page · download · from NASA CMR
Where it is published
- DOI doi.org/10.34911/rdnt.r4p1fr ↗
DOI / persistent id · from NASA CMR
Catalogue records · 2
- CMR UMM-JSON cmr.earthdata.nasa.gov/search/concepts/C2781412264-MLHUB.umm_json ↗
metadata API · from NASA CMR
- CMR record cmr.earthdata.nasa.gov/search/concepts/C2781412264-MLHUB.html ↗
catalogue entry · from NASA CMR
Topics
- Stated by source
- Airplane · Vector Labels
- Inferred from text
- Image 75%
Provenance · 1 source records, 12 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| NASA Earthdata CMR | C2781412264-MLHUB | 9 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · NASA CMR | connector:nasa_cmr@1.0.0 | |
| concepts[measured_variable].gcmd:earth-science-services/machine-learning-training-data/labels/vector-labels | source · NASA CMR | connector:nasa_cmr@1.0.0 | |
| concepts[modality].local:modality:image | enrichment · NASA CMR | keyword-concept-rules@1.0.0 | title+description (75%) |
| concepts[platform].gcmd_platform:airplane | source · NASA CMR | connector:nasa_cmr@1.0.0 | |
| created_date | source · NASA CMR | connector:nasa_cmr@1.0.0 | |
| description | source · NASA CMR | connector:nasa_cmr@1.0.0 | /umm/Abstract |
| license_text | source · NASA CMR | connector:nasa_cmr@1.0.0 | |
| publication_date | source · NASA CMR | connector:nasa_cmr@1.0.0 | |
| spatial | source · NASA CMR | connector:nasa_cmr@1.0.0 | /umm/SpatialExtent |
| temporal | source · NASA CMR | connector:nasa_cmr@1.0.0 | /umm/TemporalExtents |
| title | source · NASA CMR | connector:nasa_cmr@1.0.0 | /umm/EntryTitle |
| version_label | source · NASA CMR | connector:nasa_cmr@1.0.0 |