2024

 

Research article (journal)

Oehmcke, S., Li, L., Trepekli, K., Revenga, J. C., Nord-Larsen, T., Gieseke, F., & Igel, C. (2024). Deep point cloud regression for above-ground forest biomass estimation from airborne LiDAR. Remote Sensing of Environment, 302.
More details BibTeX Full text DOI

2023

 

Research article in proceedings (conference)

Lima, M., Denis, M. L., Christian;, G., & Fabian, (2023). End-to-End Neural Network Training for Hyperbox-Based Classification. In Proceedings of the 31th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Brügge. (accepted / in press (not yet published))
More details BibTeX Full text

Lülf, C., Lima Martins, D. M., Vaz, S. M. A., Zhou, Y., & Gieseke, F. (2023). Fast Search-By-Classification for Large-Scale Databases Using Index-Aware Decision Trees and Random Forests. In VLDB, E. (Ed.), Proceedings of the VLDB Endowment (11, pp. 2845–2857). Vancouver: Association for Computing Machinery.
More details BibTeX Full text DOI

Lülf, C., Lima Martins, D. M., Vaz, S. M. A., Zhou, Y., & Gieseke, F. (2023). RapidEarth: A Search Engine for Large-Scale Geospatial Imagery. In Proceedings of the ACM SIGSPATIAL 2023, Hamburg. (accepted / in press (not yet published))
More details BibTeX Full text

 

Research article (journal)

Li, , Sizhuo;, B., Martin;, F., Rasmus;, K., Ankit;, I., Christian;, G., Fabian;, N.-L., Thomas;, O., Stefan;, C., Ask, H. J., Samuli;, T., Xiaoye;, d., Alexandre;, C., & Philippe, (2023). Deep learning enables image-based tree counting, crown segmentation, and height prediction at national scale. PNAS Nexus, 2(4).
More details BibTeX DOI

Reiner, F., Brandt, M., Tong, X., Skole, D., Kariryaa, A., Ciais, P., Davies, A., Hiernaux, P., Chave, J., Mugabowindekwe, M., Igel, C., Oehmcke, S., Gieseke, F., Li, S., Liu, S., Saatchi, S. S., Boucher, P., Singh, J., Taugourdeau, S., Dendoncker, M., Song, X.-P., Mertz, O., Tucker, C., & Fensholt, R. (2023). More than one quarter of Africa's tree cover is found outside areas previously classified as forest. Nature Communications. (accepted / in press (not yet published))
More details BibTeX

2022

 

Research article in proceedings (conference)

Carvalho, T., Martins, D., Lima, N. F., & Vossen, G. (2022). Recommending View Bundles in Data Marketplaces. In IEEE, (Ed.), IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022, Prague, Czech Republic, October 9-12, 2022 (pp. 3403–3408). Prague, Czech Republic: IEEE.
More details BibTeX Full text DOI

Czarnowski, I., & Martins, D. (2022). Impact of Clustering on a Synthetic Instance Generation in Imbalanced Data Streams Classification. In Groen, D., Mulatier, C., Paszynski, M., Krzhizhanovskaya, V., Dongarra, J., & Sloot, P. (Eds.), Computational Science — {ICCS} 2022 — 22nd International Conference, London, UK, June 21-23, 2022, Proceedings, Part {II} (pp. 586–597). Lecture Notes in Computer Science: Vol. 13351. Cham: Springer.
More details BibTeX Full text DOI

Oehmcke, S., & Gieseke, F. (2022). Input Selection for Bandwidth-Limited Neural Network Inference. In Banerjee, A., Zhou, Z.-H., Papalexakis, E. E., & Riondato, M. (Eds.), Proceedings of the 2022 SIAM International Conference on Data Mining (SDM) (pp. 280–288). USA: Society for Industrial and Applied Mathematics (SIAM).
More details BibTeX DOI

Oehmcke, S., Li, L., Revenga, J., Nord-Larsen, T., Trepekli, K., Gieseke, F., & Igel, C. (2022). Deep Learning Based 3D Point Cloud Regression for Estimating Forest Biomass. In Renz, M., & Sarwat, M. (Eds.), 30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2022) (pp. 1–4). New York, NY, USA: Association for Computing Machinery.
More details BibTeX DOI

Souza, A. J., Martins, D., & Lima, N. F. (2022). Evolving Interpretable Classification Models via Readability-Enhanced Genetic Programming. In Ishibuchi, H., Kwoh, C.-K., Tan, A.-H., Srinivasan, D., Miao, C., Trivedi, A., & Crockett, K. (Eds.), Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence (SSCI 2022), 4 — 7 December 2022, Singapore (pp. 1691–1697). Singapur: IEEE.
More details BibTeX DOI

 

Research article (journal)

Masolele, R. N., De, S. V., Marcos, D., Verbesselt, J., Gieseke, F., Mulatu, K. A., Moges, Y., Sebrala, H., Martius, C., & Herold, M. (2022). Using high-resolution imagery and deep learning to classify land-use following deforestation: a case study in Ethiopia. GIScience and Remote Sensing, 59(1), 1446–1472.
More details BibTeX DOI

Mugabowindekwe, M., Brandt, M., Chave, J., Reiner, F., Skole, D., Kariryaa, A., Igel, C., Hiernaux, P., Ciais, P., Mertz, O., Tong, X., Li, S., Rwanyiziri, G., Dushimiyimana, T., Ndoli, A., Valens, U., Lillesø, J.-P., Gieseke, F., Tucker, C., Saatchi, S. S., & Fensholt, R. (2022). Nation-wide mapping of tree-level aboveground carbon stocks in Rwanda. Nature Climate Change, 13.
More details BibTeX DOI

Revenga, J. C., Trepekli, K., Oehmcke, S., Jensen, R., Li, L., Igel, C., Gieseke, F., & Friborg, T. (2022). Above-Ground Biomass Prediction for Croplands at a Sub-Meter Resolution Using UAV–LiDAR and Machine Learning Methods. Remote Sensing (Remote Sens.), 14(16), 3912.
More details BibTeX DOI

 

Forschungsartikel in Online-Sammlung (Konferenz)

Hellweg, T., Oehmcke, S., Kariryaa, A., Gieseke, F., & Igel, C. a. F. G. a. C. I. (2022). Ensemble Learning for Semantic Segmentation of Ancient {Maya} Architectures.
More details Full text