A Spatio-Temporal Deep Learning-Based Crop Classification Model for Satellite Imagery
ml-model cnn segmentation supervised |
First place solution for Crop Detection from Satellite Imagery competition organized by CV4A workshop at ICLR 2020. The model architecture consists of 3-layer Conv-net, Masked Features Averaging layer, 3-layer Bi-directional GRU-net and fully connected classification layer. Masked Features Averaging layer is similar to global average pooling but only averages pixels belong to crop field.
Model ID | model-cv4a-crop-detection-v1 |
DOI | 10.34911/rdnt.h28fju |
Creator | Amer, Karim |
Contact | ml@radiant.earth |
License | CC-BY-4.0 |
Applicable Temporal Extent | 2019-06-06 / 2019-11-03 |
Documentation
Publications
Amer, Karim, and Mohamed Elhelw. "A Spatio-Temporal Deep Learning-Based Crop Classification Model for Satellite Imagery." AGU Fall Meeting Abstracts. Vol. 2020. 2020.
Citation
Amer, K. (2022) “A Spatio-Temporal Deep Learning-Based Crop Classification Model for Satellite Imagery”, Version 1.0, Radiant MLHub. [Date Accessed] Radiant MLHub. https://doi.org/10.34911/rdnt.h28fju
Inferencing Image Example
docker pull docker.io/radiantearth/crop-detection-dl:1
Assets and Links
| Inference Runtime | Model Inferencing Runtime |
| Checkpoint | Final Model Checkpoint |
| Training Data | Training Data Source, Training Data Labels |
| Inferencing Image | docker.io/radiantearth/crop-detection-dl:1 |
| Related MLHub Dataset | CV4A Kenya Crop Type Competition Dataset |
CONNECT
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