Publicații

@inproceedings{11242277,

title = {Large Language Models with Retrieval Augmented Generation to streamline Sentinel-2 data Description},

author = {Teodor Costăchioiu and Corina Văduva and Daniela Faur},

doi = {10.1109/IGARSS55030.2025.11242277},

year  = {2025},

date = {2025-01-01},

booktitle = {IGARSS 2025 - 2025 IEEE International Geoscience and Remote Sensing Symposium},

pages = {1022-1026},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{6866702,

title = {Multitemporal Satellite Image Time Series analysis of urban development in Bucharest and Ilfov areas},

author = {Teodor Costăchioiu and Rodica Constantinescu and Mihai Datcu},

doi = {10.1109/ICComm.2014.6866702},

year  = {2014},

date = {2014-01-01},

booktitle = {2014 10th International Conference on Communications (COMM)},

pages = {1-4},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{8080676,

title = {Semantic analysis of EO-1 hyperion hyperspectral data},

author = {Teodor Costăchioiu and Rodica Constantinescu and Vasile Lăzărescu and Mihai Datcu},

doi = {10.1109/WHISPERS.2013.8080676},

year  = {2013},

date = {2013-01-01},

booktitle = {2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)},

pages = {1-4},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{dlr88948,

title = {Content extraction of long-term Satellite Image Time Series: spatiotemporal analysis of Bucharest metropolitan area},

author = {Teodor Costachioiu and Corina Vaduva and Vasile Lazarescu and Mihai Datcu},

url = {https://elib.dlr.de/88948/},

year  = {2013},

date = {2013-01-01},

booktitle = {MultiTemp 2013 Conference Proceedings},

pages = {1–4},

publisher = {IEEE Xplore},

abstract = {Current earth observation data repositories enable us to extract observations of the same geographical area over the course of time and to create satellite image time series (SITS). Such SITS are data sets of high complexity, embedding spatial, spectral and temporal information. 

The rich content of SITS has generated a great interest in the recent years, many SITS analysis methods being developed. Many of these methods are focused towards improving the precision of land use/land cover detection by exploiting the inherent redundancy of SITS data. 

Some other methods focus on the evolution of a specific land use/land cover type. Often these methods are applied on SITS that span over a short period of time, typical one or several years. In this paper we focus on a different use scenario of SITS analysis by extracting classes of 

dynamic evolutions from long-term satellite image time series. As such we create a SITS of over 100 Landsat datasets, spanning from 1984 to 2011, covering the metropolitan area of Bucharest, Romania – perhaps the longest SITS ever being used. The analysis of this SITS aims to evaluate the spatio-temporal evolutions, focusing on finding classes of temporal dynamics of the main land use/land cover classes of the studied area, such as urban areas, agricultural land, forestry and water bodies, identifying interesting evolutions in the studied area. Furthermore, the extracted dynamic classes are used to establish relationships with known ancillary data 

such as CORINE Land Cover vector data sets or statistical datasets for demographics of the studied area.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{dlr73740,

title = {Time evolution analysis and change detection for data mining systems},

author = {C. Vaduva and C. Patrascu and T. Costachioiu and I. Gavat and V. Lazarescu and Mihai Datcu},

url = {https://elib.dlr.de/73740/},

isbn = {978-92-79-19708-6},

year  = {2011},

date = {2011-03-01},

booktitle = {ESA-EUSC-JRC 2011 Conference Proceedings},

pages = {13–16},

publisher = {JRC},

abstract = {The paper proposes two complementary methods for change analysis and feature extraction: A time evolution analysis of satellite images and using the correlation coefficient together with conditional information.},

note = {DOI: 10.2788/69291},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1007/978-3-642-21887-3_23,

title = {Speed-Up of GIS Processing Using Multicore Architectures},

author = {Iulian Nita and Teodor Costachioiu and Vasile Lazarescu},

editor = {Beniamino Murgante and Osvaldo Gervasi and Andrés Iglesias and David Taniar and Bernady O. Apduhan},

isbn = {978-3-642-21887-3},

year  = {2011},

date = {2011-01-01},

booktitle = {Computational Science and Its Applications - ICCSA 2011},

pages = {293–302},

publisher = {Springer Berlin Heidelberg},

address = {Berlin, Heidelberg},

abstract = {Today's abilities of gathering and storing data are not matched by the ability to process this vast amount of data. Such examples can be found in the field of remote sensing, where new satellite missions contribute to a continuous downstream of remotely sensed data. Processing of large remotely sensed datasets has a high algorithmic complexity and requires considerable hardware resources. As many of the pixel-level operations are parallelizable, data processing can benefit from multicore technology. In this paper we use Dynamic Data Flow Model of Computation to create a processing framework that is both portable and scalable, being able to detect the number of processing cores and also capable to dynamically allocating tasks in such manner to ensure the balance of the processing load on each core. The proposed method is used to accelerate a water detection algorithm from Landsat TM data and was tested on multiple platforms with different multicore configurations.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{6047891,

title = {Multiprocessor real time edge detection using FPGA IP cores},

author = {Iulian Nita and Teodor Costachioiu and Vasile Lazarescu and Tiberiu Seceleanu},

doi = {10.1109/ICCP.2011.6047891},

year  = {2011},

date = {2011-01-01},

booktitle = {2011 IEEE 7th International Conference on Intelligent Computer Communication and Processing},

pages = {331-334},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}