Fabian Cristian Gieseke

Søgeresultater

• 2024

  CLIP-Branches: Interactive Fine-Tuning for Text-Image Retrieval

  Lülf, C., Lima Martins, D. M., Vaz Salles, M. A., Zhou, Y. & Gieseke, F., 2024, SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, Inc., s. 2719-2723 5 s.

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• Deep point cloud regression for above-ground forest biomass estimation from airborne LiDAR

  Oehmcke, S., Li, L., Trepekli, K., Revenga, J. C., Nord-Larsen, T., Gieseke, F. & Igel, C., 2024, I: Remote Sensing of Environment. 302, 21 s., 113968.

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  12 Citationer (Scopus)

  59 Downloads (Pure)

• Global mapping of human-transformed dike-pond systems

  Xu, Y., Feng, L., Fang, H., Song, X-P., Gieseke, F., Kariryaa, A., Oehmcke, S., Gibson, L., Jiang, X., Lin, R., Woolway, R. I., Zheng, C., Brandt, M. & Fensholt, R., 2024, I: Remote Sensing of Environment. 313, 14 s., 114354.

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  2 Citationer (Scopus)
• 2023

  Deep learning enables image-based tree counting, crown segmentation and height prediction at national scale

  Li, S., Brandt, M., Fensholt, R., Kariryaa, A., Igel, C., Gieseke, F., Nord-Larsen, T., Oehmcke, S., Carlsen, A. H., Junttila, S., Tong, X., d’Aspremont, A. & Ciais, P., 2023, I: PNAS Nexus. 2, 4, 16 s., pgad076.

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  28 Citationer (Scopus)

  134 Downloads (Pure)

• Fast Search-By-Classification for Large-Scale Databases Using Index-Aware Decision Trees and Random Forests

  Lülf, C., Mayr Lima Martins, D., Vaz Salles, M. A., Zhou, Y. & Gieseke, F. C., 2023, I: Proceedings of the VLDB Endowment. 16, 13, s. 2845–2857

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• More than one quarter of Africa's tree cover is found outside areas previously classified as forest

  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., Boucher, P., Singh, J., Taugourdeau, S., Dendoncker, M. & 4 flere, Song, X-P., Mertz, O., Tucker, C. J. & Fensholt, R., 2023, I: Nature Communications. 14, 10 s., 2258.

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  57 Citationer (Scopus)

  55 Downloads (Pure)

• Nation-wide mapping of tree-level aboveground carbon stocks in Rwanda

  Mugabowindekwe, M., Brandt, M., Chave, J., Reiner, F., Skole, D. L., Kariryaa, A., Igel, C., Hiernaux, P., Ciais, P., Mertz, O., Tong, X., Li, S., Rwanyiziri, G., Dushimiyimana, T., Ndoli, A., Uwizeyimana, V., Lillesø, J-P. B., Gieseke, F., Tucker, C. J., Saatchi, S. & 1 flere, Fensholt, R., 2023, I: Nature climate change. 13, s. 91-97

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  42 Citationer (Scopus)

  81 Downloads (Pure)

• RapidEarth: A Search-by-Classification Engine for Large-Scale Geospatial Imagery

  Lülf, C., Martins, D. M. L., Salles, M. A. V., Zhou, Y. & Gieseke, F., 2023, 31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2023. Damiani, M. L., Renz, M., Eldawy, A., Kroger, P. & Nascimento, M. A. (red.). Association for Computing Machinery, Inc., s. 1-4 58. (GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems).

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  2 Citationer (Scopus)

  42 Downloads (Pure)
• 2022

  Above-Ground Biomass Prediction for Croplands at a Sub-Meter Resolution Using UAV–LiDAR and Machine Learning Methods

  Revenga, J. C., Trepekli, K., Oehmcke, S., Jensen, R., Li, L., Igel, C., Gieseke, F. C. & Friborg, T., 2022, I: Remote Sensing. 14, 16, 22 s., 3912.

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  15 Citationer (Scopus)

  82 Downloads (Pure)

• Ensemble Learning for Semantic Segmentation of Ancient Maya Architectures

  Hellweg, T., Oehmcke, S., Kariryaa, A., Gieseke, F. & Igel, C., 2022, Discover the Mysteries of the Maya: Selected Contributions from the Machine Learning Challenge & the Discovery Challenge Workshop, ECML PKDD 2021. Kocev, D., Simidjievski, N., Kostovska, A., Dimitrovski, I. & Kokalj, Ž. (red.). Ljubljana: Jožef Stefan Institute , s. 13-19 7 s.

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• Input Selection for Bandwidth-Limited Neural Network Inference

  Oehmcke, S. & Gieseke, F., 2022, Proceedings of the 2022 SIAM International Conference on Data Mining, SDM 2022. SIAM, s. 280-288 9 s.

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• 2021

  Attentional feature fusion

  Dai, Y., Gieseke, F., Oehmcke, S., Wu, Y. & Barnard, K., 2021, Proceedings - 2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021. IEEE, s. 3559-3568

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  658 Citationer (Scopus)

• Dataset Sensitive Autotuning of Multi-versioned Code Based on Monotonic Properties: Autotuning in Futhark

  Munksgaard, P., Breddam, S. L., Henriksen, T., Gieseke, F. C. & Oancea, C., 2021, Trends in Functional Programming - 22nd International Symposium, TFP 2021, Revised Selected Papers. Zsok, V. & Hughes, J. (red.). Springer Science and Business Media Deutschland GmbH, s. 3-23 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 12834 LNCS).

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  2 Citationer (Scopus)

  34 Downloads (Pure)

• Estimating Forest Canopy Height with Multi-Spectral and Multi-Temporal Imagery Using Deep Learning

  Oehmcke, S., Nyegaard-Signori, T., Grogan, K. & Gieseke, F., 2021, Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021. Chen, Y., Ludwig, H., Tu, Y., Fayyad, U., Zhu, X., Hu, X. T., Byna, S., Liu, X., Zhang, J., Pan, S., Papalexakis, V., Wang, J., Cuzzocrea, A. & Ordonez, C. (red.). Institute of Electrical and Electronics Engineers Inc., s. 4915-4924 10 s.

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  1 Citationer (Scopus)

• Plant Structure and Carbon Storage Assessment Utilizing Drone-Borne Lidar and Deep Learning Technologies in a Danish Agricultural Expanse.

  Trepekli, K., Revenga, J. C., Oehmcke, S., Gieseke, F., Jensen, R. & Friborg, T., 2021. 12 s.

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• Prediction of above ground biomass and C-stocks based on UAV-LiDAR,multispectral imagery and machine learning methods.

  Revenga, J. C., Trepekli, K., Oehmcke, S., Gieseke, F., Jensen, R. & Friborg, T., 2021. 1 s.

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• 2020

  An unexpectedly large count of trees in the West African Sahara and Sahel

  Brandt, M., Tucker, C. J., Kariryaa, A., Rasmussen, K., Abel, C., Small, J., Chave, J., Rasmussen, L. V., Hiernaux, P., Diouf, A. A., Kergoat, L., Mertz, O., Igel, C., Gieseke, F., Schöning, J., Li, S., Melocik, K., Meyer, J., Sinno, S., Romero, E. & 4 flere, Glennie, E., Montagu, A., Dendoncker, M. & Fensholt, R., 2020, I: Nature. 587, s. 78–82

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  284 Citationer (Scopus)

• Approximate Nearest-Neighbour Fields via Massively-Parallel Propagation-Assisted K-D Trees

  Oancea, C. E., Robroek, T. & Gieseke, F. C., 2020, Proceedings of the IEEE International Conference on Big Data (BigData2020). 10 udg. IEEE, s. 5172-5181

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• Attention as activation

  Dai, Y., Oehmcke, S., Gieseke, F., Wu, Y. & Barnard, K., 2020, Proceedings of ICPR 2020 - 25th International Conference on Pattern Recognition. IEEE, s. 4131-4136 6 s. 9413020

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  2 Citationer (Scopus)

• Creating cloud-free satellite imagery from image time series with deep learning

  Oehmcke, S., Chen, T. H. K., Prishchepov, A. V. & Gieseke, F., 3 nov. 2020, Proceedings of the 9th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BIGSPATIAL 2020. Chandola, V., Vatsavai, R. R. & Shashidharan, A. (red.). Association for Computing Machinery, 10 s. 3

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  12 Citationer (Scopus)

  165 Downloads (Pure)

• Massively-parallel change detection for satellite time series data with missing values

  Gieseke, F., Rosca, S., Henriksen, T., Verbesselt, J. & Oancea, C. E., 2020, Proceedings - 2020 IEEE 36th International Conference on Data Engineering, ICDE 2020. IEEE, s. 385-396 9101616

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  8 Citationer (Scopus)
• 2019

  Bigger buffer k-d trees on multi-many-core systems

  Gieseke, F. C., Oancea, C. E., Mahabal, A., Igel, C. & Heskes, T., 2019, High Performance Computing for Computational Science – VECPAR 2018: 13th International Conference, São Pedro, Brazil, September 17–19, 2018, Revised Selected Papers. Springer, s. 202-214 (Lecture Notes in Computer Science, Bind 11333).

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  1 Citationer (Scopus)

• Detecting Hardly Visible Roads in Low-Resolution Satellite Time Series Data

  Oehmcke, S., Thrysøe, C., Borgstad, A., Vaz Salles, M. A., Brandt, M. S. & Gieseke, F. C., 2019, Proceedings of the IEEE International Conference on Big Data, Big Data 2019: Special Session on Intelligent Data Mining. IEEE, s. 2403-2412 9006251,

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  17 Citationer (Scopus)

• Magnitude and Uncertainty Pruning Criterion for Neural Networks

  Ko, V., Oehmcke, S. & Gieseke, F., 2019, 2019 IEEE International Conference on Big Data, Big Data. Baru, C., Huan, J., Khan, L., Hu, X. T., Ak, R., Tian, Y., Barga, R., Zaniolo, C., Lee, K. & Ye, Y. F. (red.). IEEE, s. 2317-2326 9005692. (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019).

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  4 Citationer (Scopus)
• 2018

  Artistic movement recognition by consensus of boosted SVM based experts

  Florea, C. & Gieseke, F., 2018, I: Journal of Visual Communication and Image Representation. 56, s. 220-233

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  12 Citationer (Scopus)

• Massively-parallel break detection for satellite data

  von Mehren, M., Gieseke, F., Verbesselt, J., Rosca, S., Horion, S. & Zeileis, A., 2018, SSDBM '18 Proceedings of the 30th International Conference on Scientific and Statistical Database Management . Association for Computing Machinery, Bind Part F137913. 10 s. 5

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  2 Citationer (Scopus)

• Training big random forests with little resources

  Gieseke, F. & Igel, C., 2018, KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Association for Computing Machinery, s. 1445-1454

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  16 Citationer (Scopus)
• 2017

  A probabilistic approach to emission-line galaxy classification

  de Souza, R. S., Dantas, M. L. L., Costa-Duarte, M. V., Feigelson, E. D., Killedar, M., Lablanche, P. Y., Vilalta, R., Krone-Martins, A., Beck, R. & Gieseke, F., 1 dec. 2017, I: Monthly Notices of the Royal Astronomical Society. 472, 3, s. 2808-2822 15 s.

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  30 Citationer (Scopus)

• Artistic movement recognition by boosted fusion of color structure and topographic description

  Florea, C., Toca, C. & Gieseke, F. C., 11 maj 2017, Proceedings - 2017 IEEE Winter Conference on Applications of Computer Vision. IEEE, s. 569-577 9 s.

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  22 Citationer (Scopus)

• Big universe, big data: machine learning and image analysis for astronomy

  Kremer, J., Stensbo-Smidt, K., Gieseke, F. C., Pedersen, K. S. & Igel, C., 2017, I: IEEE Intelligent Systems. 32, 2, s. 16-22 7 s.

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  73 Citationer (Scopus)

  299 Downloads (Pure)

• bufferkdtree: a Python library for massive nearest neighbor queries on multi-many-core devices

  Gieseke, F. C., Oancea, C. E. & Igel, C., 15 mar. 2017, I: Knowledge-Based Systems. 120, s. 1-3 3 s.

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  2 Citationer (Scopus)

• On the realistic validation of photometric redshifts

  COIN Collaboration, 1 jul. 2017, I: Monthly Notices of the Royal Astronomical Society. 468, 4, s. 4323-4339 17 s.

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  49 Citationer (Scopus)

• Sacrificing information for the greater good: how to select photometric bands for optimal accuracy

  Stensbo-Smidt, K., Gieseke, F. C., Igel, C., Zirm, A. W. & Pedersen, K. S., 2017, I: Monthly Notices of the Royal Astronomical Society. 464, 3, s. 2577-2596 20 s.

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  16 Citationer (Scopus)
• 2016

  Exploring the spectroscopic diversity of Type Ia supernovae with DRACULA: a machine learning approach

  Sasdelli, M., Ishida, E. E. O., Vilalta, R., Aguena, M., Busti, V. C., Camacho, H., Trindade, A. M. M., Gieseke, F., de Souza, R. S., Fantaye, Y. T. & Mazzali, P. A., 2016, I: Monthly Notices of the Royal Astronomical Society. 461, 2, s. 2044-2059 16 s.

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  25 Citationer (Scopus)

• Parallelized rotation and flipping INvariant Kohonen maps (PINK) on GPUs

  Polsterer, K. L., Gieseke, F. C., Igel, C., Doser, B. & Gianniotis, N., 2016, Proceedings of the 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2016). i6doc.com, s. 405-410 6 s.

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• Uncertain photometric redshifts

  Polsterer, K. L., D'Isanto, A. & Gieseke, F., 2016, (Accepteret/In press). 11 s.

  Publikation: Konferencebidrag › Paper › Forskning
• 2015

  Analyzing complex and structured data via unsupervised learning techniques

  Polsterer, K. L., Gieseke, F., Gianniotis, N. & Kügler, D., 2015.

  Publikation: Konferencebidrag › Paper › Forskning

• An efficient many-core implementation for semi-supervised support vector machines

  Gieseke, F., 2015, Machine Learning, Optimization, and Big Data: First International Workshop, MOD 2015, Taormina, Sicily, Italy, July 21-23, 2015, Revised Selected Papers. Pardalos, P., Pavone, M., Farinella, G. M. & Cutello, V. (red.). Springer, s. 145-157 13 s. (Lecture notes in computer science, Bind 9432).

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  1 Citationer (Scopus)

• Automatic galaxy classification via machine learning techniques: Parallelized rotation/flipping INvariant Kohonen maps (PINK)

  Polsterer, K. L., Gieseke, F. C. & Igel, C., 2015, Astronomical Data Analysis Software and Systems: Proceedings of a meeting held at Calgary, Alberta, Canada 5-9 October 2014. Taylor, A. R. & Rosolowsky, E. (red.). Astronomical Society of the Pacific, Bind 24. s. 81-86 6 s. (ASP Conference Series, Bind 495).

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• Batch steepest-descent-mildest-ascent for interactive maximum margin clustering

  Gieseke, F., Pahikkala, T. & Heskes, T., 2015, Advances in Intelligent Data Analysis XIV: 14th International Symposium, IDA 2015, Saint Etienne. France, October 22 -24, 2015. Proceedings. Fromont, E., De Bie, T. & van Leeuwen, M. (red.). Springer, s. 95-107 13 s. (Lecture notes in computer science, Bind 9385).

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  1 Citationer (Scopus)

• DRACULA: Dimensionality Reduction And Clustering for Unsupervised Learning in Astronomy

  Aguena, M., Busti, V. C., Camacho, H., Sasdelli, M., Ishida, E. E. O., Vilalta, R., Trindade, A. M. M., Gieseke, F., de Souza, R. S. & Fantaye, Y. T., 2015

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• Nearest neighbor density ratio estimation for large-scale applications in astronomy

  Kremer, J., Gieseke, F., Pedersen, K. S. & Igel, C., 2015, I: Astronomy and Computing. 12, s. 67-72 6 s.

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  14 Citationer (Scopus)
• 2014

  A framework for data mining in wind power time series

  Kramer, O., Gieseke, F. C., Heinermann, J., Poloczek, J. & Treiber, N. A., 2014, Data analytics for renewable energy integration: Second ECML PKDD Workshop, DARE 2014, Nancy, France, September 19, 2014, Revised Selected Papers. Woon, W. L., Aung, Z. & Madnick, S. (red.). Springer, s. 97-107 (Lecture notes in computer science, Bind 8817).

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  3 Citationer (Scopus)

• Buffer k-d trees: processing massive nearest neighbor queries on GPUs

  Gieseke, F. C., Heinermann, J., Oancea, C. E. & Igel, C., 2014, Proceedings of the 31^st International Conference on Machine Learning, Beijing, China, 2014. 9 s. (JMLR: Workshop and Conference Proceedings, Bind 32).

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• Fast and simple gradient-based optimization for semi-supervised support vector machines

  Gieseke, F., Airola, A., Pahikkala, T. & Kramer, O., 2014, I: Neurocomputing. 123, s. 23-32 10 s.

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  52 Citationer (Scopus)

• Improving the performance of photometric regression models via massive parallel feature selection

  Polsterer, K. L., Gieseke, F. C., Igel, C. & Goto, T., 2014, Astronomical Data Analysis Software and Systems XXIII. Manset, N. & Forshay, P. (red.). Astronomical Society of the Pacific, Bind 485. s. 425-428 4 s. (Astronomical Society of the Pacific. Conference Proceedings, Bind 485).

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• On unsupervised training of multi-class regularized least-squares classifiers

  Pahikkala, T., Airola, A., Gieseke, F. C. & Kramer, O., 2014, I: Journal of Computer Science and Technology. 29, 1, s. 90-104 15 s.

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  1 Citationer (Scopus)

• Speedy greedy feature selection: better redshift estimation via massive parallelism

  Gieseke, F. C., Polsterer, K. L., Oancea, C. E. & Igel, C., 2014, ESANN 2014 proceedings: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen, M. (red.). i6doc.com, s. 87-92 6 s.

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  7 Citationer (Scopus)
• 2013

  Finding new high-redshift quasars by asking the neighbours

  Polsterer, K. L., Zinn, P. C. & Gieseke, F., 2013, I: Monthly Notices of the Royal Astronomical Society. 428, 1, s. 226-235 10 s.

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  18 Citationer (Scopus)

• From supervised to unsupervised support vector machines and applications in astronomy

  Gieseke, F., 2013, I: KI - Künstliche Intelligenz. 27, 3, s. 281-285 5 s.

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  1 Citationer (Scopus)