All Outputs (6)

Resolving data gaps in global surface water monthly records through a self-supervised deep learning strategy (2024)
Journal Article
Hao, Z., Cai, X., Ge, Y., Foody, G., Li, X., Yin, Z., Du, Y., & Feng. (2024). Resolving data gaps in global surface water monthly records through a self-supervised deep learning strategy. Journal of Hydrology, 640, Article 131673. https://doi.org/10.1016/j.jhydrol.2024.131673

The distribution of land surface water bodies is constantly changing. Monitoring these changes is critical for both humanity and the ecological system. The Joint Research Centre Global Surface Water (GSW) dataset is crucial in monitoring global water... Read More about Resolving data gaps in global surface water monthly records through a self-supervised deep learning strategy.

Under the mantra: 'Make use of colorblind friendly graphs' (2024)
Journal Article
Rocchini, D., Chieffallo, L., Thouverai, E., D'Introno, R., Dagostin, F., Donini, E., Foody, G., Garnier, S., Mazzochini, G. G., Moudry, V., Rudis, B., Simova, P., Torresani, M., & Nowosad, J. (in press). Under the mantra: 'Make use of colorblind friendly graphs'. Environmetrics,

Colorblindness is a genetic condition that affects a person's ability to accurately perceive colors. Several papers still exist making use of rainbow colors palette to show output. In such cases, for colorblind people such graphs are meaningless. In... Read More about Under the mantra: 'Make use of colorblind friendly graphs'.

Reviewing the Spectral Variation Hypothesis: Twenty years in the tumultuous sea of biodiversity estimation by remote sensing (2024)
Journal Article
Torresani, M., Rossi, C., Perrone, M., Hauser, L. T., Féret, J.-B., Moudrý, V., Simova, P., Ricotta, C., Foody, G. M., Kacic, P., Feilhauer, H., Malavasi, M., Tognetti, R., & Rocchini, D. (2024). Reviewing the Spectral Variation Hypothesis: Twenty years in the tumultuous sea of biodiversity estimation by remote sensing. Ecological Informatics, 82, Article 102702. https://doi.org/10.1016/j.ecoinf.2024.102702

Twenty years ago, the Spectral Variation Hypothesis (SVH) was formulated as a means to link between different aspects of biodiversity and spatial patterns of spectral data (e.g. reflectance) measured from optical remote sensing. This hypothesis initi... Read More about Reviewing the Spectral Variation Hypothesis: Twenty years in the tumultuous sea of biodiversity estimation by remote sensing.

DeepWaterFraction: A globally applicable, self-training deep learning approach for percent surface water area estimation from Landsat mission imagery (2024)
Journal Article
Hao, Z., Foody, G., Ge, Y., Cai, X., Du, Y., & Ling, F. (2024). DeepWaterFraction: A globally applicable, self-training deep learning approach for percent surface water area estimation from Landsat mission imagery. Journal of Hydrology, 638, Article 131512. https://doi.org/10.1016/j.jhydrol.2024.131512

Surface water area estimation is essential for understanding global environmental dynamics, yet it presents significant challenges, particularly when dealing with small water bodies like ponds and narrow width rivers. Surface water areas for these sm... Read More about DeepWaterFraction: A globally applicable, self-training deep learning approach for percent surface water area estimation from Landsat mission imagery.

The effects of extreme heat on human health in tropical Africa (2024)
Journal Article
Kunda, J. J., Gosling, S. N., & Foody, G. M. (2024). The effects of extreme heat on human health in tropical Africa. International Journal of Biometeorology, 68, 1015-1033. https://doi.org/10.1007/s00484-024-02650-4

This review examines high-quality research evidence that synthesises the effects of extreme heat on human health in tropical Africa. Web of Science (WoS) was used to identify research articles on the effects extreme heat, humidity, Wet-bulb Globe Tem... Read More about The effects of extreme heat on human health in tropical Africa.

Ground Truth in Classification Accuracy Assessment: Myth and Reality (2024)
Journal Article
Foody, G. M. (2024). Ground Truth in Classification Accuracy Assessment: Myth and Reality. Geomatics, 4(1), 81-90. https://doi.org/10.3390/geomatics4010005

The ground reference dataset used in the assessment of classification accuracy is typically assumed implicitly to be perfect (i.e., 100% correct and representing ground truth). Rarely is this assumption valid, and errors in the ground dataset can cau... Read More about Ground Truth in Classification Accuracy Assessment: Myth and Reality.