This github page contains outputs of GeoAI research conducted under the supervision of Prof. Dr. Elif SERTEL at Istanbul Technical University.

We have been sharing repos of our ongoing activities that include our codes and papers on Geospatial Artificial Intelligence topic specifically on Remote Sensing Data and Historical Maps.

We have been collaborating with several researchers including Academics, Master/PhD students, and Post-Doctoral fellows since 2016.

You can also reach us from "remotesensingandai@gmail.com" or directly e-mail to Prof. Sertel from "sertele@itu.edu.tr"

 Sertel E. and Topgul, S.N. (2025). Comparative Analysis of Deep Learning Approaches for Forest Stand Type Classification: Insights from the New VHRTreeSpecies Benchmark Dataset, International Journal of Digital Earth, 18(1). https://doi.org/10.1080/17538947.2025.2522394.

 Sür, İ. B., Algancı, U., & Sertel, E. (2025). Evaluating the performance of deep learning-based segmentation algorithms for land use land cover mapping in a heterogenous vegetative environment. International Journal of Engineering and Geosciences, 10(3), 380-397. https://doi.org/10.26833/ijeg.1528938

 Topgül¸ SN., Sertel, E., Aksoy, S., Ünsalan, C. and Fransson, JES (2024) VHRTrees: a new benchmark dataset for tree detection in satellite imagery and performance evaluation with YOLO-based models. Front. For. Glob. Change 7:1495544. https://doi.org/10.3389/ffgc.2024.1495544.

 Sertel, E., Hucko, CM., Kabadayı, ME. Automatic Road Extraction from Historical Maps Using Transformer-Based SegFormers. ISPRS International Journal of Geo-Information. 2024; 13(12):464. https://doi.org/10.3390/ijgi13120464

 Sertel, E., Kabadayı, M.E, Sengul, G. S. & Tumer, I. N, (2024). HexaLCSeg: A Historical Benchmark Dataset from Hexagon Satellite Images for Land Cover Segmentation, IEEE Geoscience and Remote Sensing Magazine, 12 (3), 197-206. https://doi.org/10.1109/MGRS.2024.3394248.

 Aksoy, S., Sertel, E., Roscher, R., Tanik, AG. & Hamzehpour, N. (2024). Thorough Assessment of Soil Salinity Using Explainable Machine Learning Methods and Landsat 8 Images, International Journal of Applied Earth Observation and Geoinformation, 130, 103879, https://doi.org/10.1016/j.jag.2024.103879.

 Bakirman, T., and Sertel, E. (2023). A benchmark dataset for deep learning-based airplane detection: HRPlanes. International Journal of Engineering and Geosciences, 8(3), 212-223. https://doi.org/10.26833/ijeg.1107890

 Wang, P. and Sertel, E. (2023). Multi-Frame Super-resolution of Remote Sensing Images using attention-based GAN models, 110387, Knowledge-Based Systems, https://doi.org/10.1016/j.knosys.2023.110387.

 Wang, P., Bayram, B. & Sertel, E., (2022). A comprehensive review on Deep Learning based Remote Sensing Image Super-Resolution Methods, Earth-Science Reviews, Invited Review Article, vol.232, 104110, https://doi.org/10.1016/j.earscirev.2022.104110.

 Bakirman, T., Komurcu, I. & Sertel, E., (2022) Comparative analysis of deep learning-based building extraction methods with the new VHR Istanbul dataset, Experts Systems with Applications, vol. 202, 117346, https://doi.org/10.1016/j.eswa.2022.117346.

 Sertel, E., Ekim, B., Osgouei, P. E., & Kabadayi, M.E., (2022). Land Use and Land Cover Mapping Using Deep Learning Based Segmentation Approaches and VHR Worldview-3 Images, Remote Sensing, 2022, 14, 4558, https://doi.org/10.3390/rs14184558.

 Ekim, B. and Sertel, E. (2021). Deep Neural Network Ensembles for Remote Sensing Land Cover and Land Use Classification, International Journal of Digital Earth, https://doi.org/10.1080/17538947.2021.1980125.

 Wang, P. and Sertel, E. (2021). Channel-spatial Attention-based Pan-sharpening of Very High-resolution Satellite Images, Knowledge-Based Systems, vol. 229, 107324, https://doi.org/10.1016/j.knosys.2021.107324.

 Ekim, B, Sertel, E. & Karadayi, M. E. (2021). Automatic Road Extraction from Historical Maps using Deep Learning Techniques: A Regional Case Study of Turkey in a German World War II Map, ISPRS International Journal of Geo-Information, vol. 10, no.8, 492; https://doi.org/10.3390/ijgi10080492.

 Wang, P., Bayram, B. & Sertel, E. (2021). Super-Resolution of Remotely Sensed Data Using Channel Attention Based Deep Learning Approach. International Journal of Remote Sensing, vol.42, no.16, 6050-6067. https://doi.org/10.1080/01431161.2021.1934598.

 Özçelik, F., Algancı, U., Sertel, E., & Ünal, G., (2021). Rethinking CNN-Based Pan-sharpening: Guided Colorization of Panchromatic Images via GANs. IEEE Transactions on Geoscience And Remote Sensing, vol.59, no.4, 3486-3501. https://doi.org/10.1109/TGRS.2020.3010441.

 Wang, P., Alganci, U. & Sertel, E, (2021). Comparative Analysis on Deep Learning based Pan-sharpening of Very High-Resolution Satellite Images. International Journal of Environment and Geoinformatics, vol. 8, no. 2, 150-165. https://doi.org/10.30897/ijegeo.834760.

 Ekim, B. and Sertel, E., (2021). A Multi-Task Deep Learning Framework for Building Footprint Segmentation, International Geoscience and Remote Sensing Symposium (IGARSS-2021), 11-16 July, Brussels, Belgium.

 Algancı, U., Soydas, M., & Sertel, E., (2020). Comparative Research on Deep Learning Approaches for Airplane Detection from Very High-Resolution Satellite Images. Remote Sensing, vol.12, no.3. https://doi.org/10.3390/rs12030458.

 Tuna, C., Ünal, G., & Sertel, E., (2018). Single-frame super resolution of remote-sensing images by convolutional neural networks. International Journal of Remote Sensing, vol.39, no.8, 2463-2479. https://doi.org/10.1080/01431161.2018.1425561.

 Tuna, C., Akoguz, A., Unal, G., and Sertel, E. “Resolution enhancement of tri-stereo remote sensing images by super resolution methods”. SPIE Remote Sensing Conference, 26-29 September 2016, Edinburgh, UK. https://doi.org/10.1117/12.2241176.

 Peijuan Wang

 Burak Ekim

 Cengiz Avcı

 Furkan Uysal