Update README.md

README.md

@@ -1,5 +1,52 @@
----
-license: other
-license_name: nicfi
-license_link: https://planet.widen.net/s/zfdpf8qxwk/participantlicenseagreement_nicfi_2024
----
+---
+license: other
+license_name: nicfi
+license_link: https://planet.widen.net/s/zfdpf8qxwk/participantlicenseagreement_nicfi_2024
+task_categories:
+- image-segmentation
+---
+
+### Summary
+
+Rehosted from: https://github.com/agroimpacts/lacunalabels/
+
+This repository hosts the datasets resulting from a project to generate a continent-wide set of crop field labels for Africa covering the years 2017-2023. The data are intended for training and assessing machine learning models that can be used to map agricultural fields over large areas and multiple years.
+
+The project was funded by the [Lacuna Fund](https://lacunafund.org/), and led by [Farmerline](https://farmerline.co/), in collaboration with [Spatial Collective](https://spatialcollective.com/) and the [Agricultural Impacts Research Group](agroimpacts.info) at [Clark University](https://www.clarku.edu/departments/geography/).
+
+Please refer to the [technical report](notebooks/report/technical-report.pdf) for more details on the methods used to develop the dataset, an analysis of label quality, and usage guidelines. The report and additional documents, analyses, and demonstration code used to develop labels by cloning the repository.
+
+
+### Cite 
+
+If you use the dataset in `lacuna-field-boundaries.tar.gz` please cite the follow:
+
+```bibtex
+@article{estes2024region,
+  title={A region-wide, multi-year set of crop field boundary labels for Africa},
+  author={Estes, LD and Wussah, A and Asipunu, M and Gathigi, M and Kova{\v{c}}i{\v{c}}, P and Muhando, J and Yeboah, BV and Addai, FK and Akakpo, ES and Allotey, MK and others},
+  journal={arXiv preprint arXiv:2412.18483},
+  year={2024}
+}
+```
+
+
+If you use the dataset in `extra.tar.gz` please cite the follow:
+
+```bibtex
+@article{khallaghi2025generalization,
+  title={Generalization Enhancement Strategies to Enable Cross-Year Cropland Mapping with Convolutional Neural Networks Trained Using Historical Samples},
+  author={Khallaghi, Sam and Abedi, Rahebeh and Abou Ali, Hanan and Alemohammad, Hamed and Dziedzorm Asipunu, Mary and Alatise, Ismail and Ha, Nguyen and Luo, Boka and Mai, Cat and Song, Lei and others},
+  journal={Remote Sensing},
+  volume={17},
+  number={3},
+  pages={474},
+  year={2025},
+  publisher={MDPI}
+}
+```
+
+
+### License
+
+The datasets contain Planet imagery which are made available under the [Norway’s International Climate and Forests Initiative (NICFI) license](https://planet.widen.net/s/zfdpf8qxwk/participantlicenseagreement_nicfi_2024).