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))
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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.
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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))
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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).
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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))
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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.
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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.
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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).
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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.
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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.
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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.
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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.
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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.
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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.
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2021

 

Research article in proceedings (conference)

Carvalho, T., Martins, D., & Lima, N. F. (2021). MapView: Exploring Datasets via Unsupervised View Recommendation. In unknown, u., & , (Eds.), {IEEE} Latin American Conference on Computational Intelligence, {LA-CCI} 2021, Temuco, Chile, November 2-4, 2021 (pp. 1–6). Temuco, Chile: IEEE.
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Dai, Y., Gieseke, F., Oehmcke, S., Wu, Y., & Barnard, K. (2021). Attentional Feature Fusion. In Proceedings of the Workshop on Applications of Computer Vision (WACV), Waikoloa, Hawaii, 3559–3568.
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Munksgaard, P., Breddam, S., Henriksen, T., Gieseke, F., & Oancea, C. E. (2021). Dataset Sensitive Autotuning of Multi-versioned Code Based on Monotonic Properties — Autotuning in Futhark. In Proceedings of the 22nd International Symposium on Trends in Functional Programming (TFP), Virtual Event, 3–23.
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Oehmcke, S., Nyegaard-Signori, T., Grogan, K., & Gieseke, F. (2021). Estimating Forest Canopy Height With Multi-Spectral and Multi-Temporal Imagery Using Deep Learning. In Chen, Y., Ludwig, H., Tu, Y., Fayyad, U. M., Zhu, X., Hu, X., Byna, S., Liu, X., Zhang, J., Pan, S., Papalexakis, V., Wang, J., Cuzzocrea, A., & Ordonez, C. (Eds.), 2021 {IEEE} International Conference on Big Data (Big Data) (pp. 4915–4924). Orlando, US: Wiley-IEEE Press.
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Silva, R. E., Martins, D., & Lima, N. F. (2021). Automatic Feature Engineering Using Self-Organizing Maps. In Unknown, U., & , (Eds.), {IEEE} Latin American Conference on Computational Intelligence, {LA-CCI} 2021, Temuco, Chile, November 2-4, 2021 (pp. 1–6). Temuco, Chile: IEEE.
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Silva, R. E., Martins, D., & Lima, N. F. (2021). Self-Organizing Transformations for Automatic Feature Engineering. In unknown, u., & , (Eds.), 2021 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1–7). Orlando: IEEE.
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Abstract in Online-Sammlung (Konferenz)

Lechtenbörger, J. (2021). Infrastructure and lightweight markup language for OER: The case of emacs-reveal (abstract). Poster session presented at the OERxDomains, Online.
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Revenga, J., Trepekli, K., Oehmcke, S., Gieseke, F., Igel, C., Jensen, R., & Friborg, T. (2021). Prediction of above ground biomass and C-stocks based on UAV-LiDAR multispectral imagery and machine learning methods. Poster session presented at the EGU General Assembly 2021, Virtual Event.
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Research article (journal)

Masolele, R. N., De, S. V., Herold, M., Marcosa, D., Verbesselt, J., Gieseke, F., Mullissa, A. G., & Martius, C. (2021). Spatial and temporal deep learning methods for deriving land-use following deforestation: A pan-tropical case study using Landsat time series. Remote Sensing of Environment, 264, 112600.
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