People

Segun (Micheal) Adedapo
CFANR-UAM Alumni: Class of 2024

Graduated: Spring 2024

Research topic: "Characterization of FOAgREC Soils and Development of Fine-Scale Soil Properties Maps".

Peer-reviewed Publications (#5):

(#1) Article, (#3) Conference Proceedings, and (#1) Book Chapter

Adedapo*, S.M., H.A., Zurqani, R.L., Ficklin, and D.C., Osborne. 2025. Comparison of Three Supervised Machine Learning Methods for Digital Soil Mapping in Highly Dense Forest Vegetation Environment. In Zurqani H.A. (ed) Geospatial artificial intelligence in Environmental and Natural Resources Management. Earth and Environmental Sciences Library. Switzerland. Springer International Publishing AG. (In press). (Book Chapter)
Adedapo*, S.M., and H.A. Zurqani. 2024. Evaluating the Performance of Various Interpolation Techniques on Digital Elevation Models in Highly Dense Forest Vegetation Environment. Ecological Informatics, 81, 102646.
Adedapo*, S.M., H.A., Zurqani, M. Blazier, J. McAlpine, and K. Cunningham. 2024. Early Detection of Pine Disease in Southeast US Forests: A Deep Learning Approach Using UAV Imagery. The 2024 International Conference on Smart Computing, IoT and Machine Learning (SIML), Surakarta, Indonesia, 2024, pp. 256-261, doi: 10.1109/SIML61815.2024.10578138. (Conference Proceeding) "Won the best presenter award".
Adedapo*, S.M., H.A., Zurqani, R.L., Ficklin, and D.C., Osborne. 2024. Applications of Machine Learning Techniques in Predicting Selected Soil Properties in Lower Mississippi Alluvial Valley Green Tree Reservoir. In IEEE Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS), Oran, Algeria, 2024, pp. 487-491, doi:10.1109/M2GARSS57310.2024.10537504. (Conference Proceeding)
Adedapo*, S.M., and H.A. Zurqani. 2023. Development of Digital Terrain Model Under High Dense Forest Cover Using USGS and Drone LiDAR Data. In IGARSS 2023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, USA, pp. 1126-1129, doi:10.1109/IGARSS52108.2023.10282409. (Conference Proceeding)

Abstracts and Posters:

Adedapo*, S.M., H.A., Zurqani, M. Blazier, J. McAlpine, and K. Cunningham. 2024. Early Detection of Pine Disease in Southeast US Forests: A Deep Learning Approach Using UAV Imagery. The 2024 International Conference on Smart Computing, IoT and Machine Learning (SIML), Surakarta, Indonesia, June 6-7, 2024. "Won the best presenter award".
Adedapo*, S.M., H.A., Zurqani, R.L., Ficklin, and D.C., Osborne. 2024. Applications of Machine Learning Techniques in Predicting Selected Soil Properties in Lower Mississippi Alluvial Valley Green Tree Reservoir. The IEEE Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS 2024), Oran, Algeria, April 15 - 17, 2024
Adedapo*, S.M., H.A., Zurqani, R.L., Ficklin, and D.C., Osborne. 2023. Application of Machine Learning in Predicting Selected Soil Properties in Lower Mississippi Alluvial Valley Green Tree Reservoir. The 14th Southern Forestry and Natural Resource Management GIS conference (SOFOR GIS), Athens, Georgia, December 11–12, 2023
Adedapo*, S.M., and Zurqani, H.A. 2023. Development of Digital Terrain Model Under High Dense Forest Cover Using USGS and Drone LiDAR Data. The 43rd annual International Geoscience and Remote Sensing Symposium (IGARSS), Pasadena, CA, July 16 - 21, 2023
Adedapo*, S.M., and Zurqani, H.A. 2023. A Comparative Analysis of Spatial Interpolation Methods on Digital Terrain Models Under High Dense Forest Cover. The Arkansas GIS Users Forum, the Arkansas GIS Spring Meeting, Jacksonville, AR, April 12, 2023