Hsu, Chia-Yu, and Li, Wenwen. Explainable GeoAI: can saliency maps help interpret artificial intelligence’s learning process? An empirical study on natural feature detection. Retrieved from https://par.nsf.gov/biblio/10469663. International Journal of Geographical Information Science 37.5 Web. doi:10.1080/13658816.2023.2191256.

Hsu, Chia-Yu, & Li, Wenwen. Explainable GeoAI: can saliency maps help interpret artificial intelligence’s learning process? An empirical study on natural feature detection. International Journal of Geographical Information Science, 37 (5). Retrieved from https://par.nsf.gov/biblio/10469663. https://doi.org/10.1080/13658816.2023.2191256

Hsu, Chia-Yu, and Li, Wenwen. "Explainable GeoAI: can saliency maps help interpret artificial intelligence’s learning process? An empirical study on natural feature detection". International Journal of Geographical Information Science 37 (5). Country unknown/Code not available: Taylor & Francis. https://doi.org/10.1080/13658816.2023.2191256. https://par.nsf.gov/biblio/10469663.

@article{osti_10469663,
place = {Country unknown/Code not available}, title = {Explainable GeoAI: can saliency maps help interpret artificial intelligence’s learning process? An empirical study on natural feature detection}, url = {https://par.nsf.gov/biblio/10469663}, DOI = {10.1080/13658816.2023.2191256}, abstractNote = {}, journal = {International Journal of Geographical Information Science}, volume = {37}, number = {5}, publisher = {Taylor & Francis}, author = {Hsu, Chia-Yu and Li, Wenwen}, }