@conference {948,
	title = {An Interactive Decision Support System for Analyzing and Linkage of Weather-Related Restrictions of Opencast Lignite Mines},
	booktitle = {LEVIA{\textquoteright}22: Leipzig Symposium on Visualization in Applications 2022},
	year = {2022},
	author = {Yves Annanias and Jonah Windolph and Robert Wehlitz and Daniel Wiegreffe}
}
@article {945,
	title = {An Interactive Decision Support System for Land Reuse Tasks},
	journal = {IEEE Computer Graphics and Applications},
	volume = {42},
	year = {2022},
	month = {11/2022},
	chapter = {72-83},
	issn = {1558-1756},
	doi = {10.1109/MCG.2022.3175604},
	author = {Yves Annanias and Dirk Zeckzer and Gerik Scheuermann and Daniel Wiegreffe}
}
@conference {915,
	title = {Report on the Correction of Erroneous Geometry Data in Land Reuse Project},
	booktitle = {Workshop on Systems to Support Renaturation Projects (SyRePro 2021) },
	year = {2021},
	abstract = {Land Reuse processes are large planning and decision-making processes based on a large
amount of geographic data. Therefore, it is essential that this data is as accurate as possible. However,
errors can occur during the creation of the data and not all of them are directly noticeable. We report
here what errors we have encountered while working with this geographic data, what problems they
can cause, and how we have fixed them. Since the correction can be very time-consuming with the
enormous amount of data, we have focused on an automatic correction. Not all of this data can be
corrected this way, for the rest, we briefly indicate a procedure to support and simplify the manual
correction.},
	author = {Yves Annanias and Marc Wahsner and Gerik Scheuermann and Daniel Wiegreffe}
}
@conference {914,
	title = {Supporting Land Reuse of Former Open Pit Mining Sites using Text Classification and Active Learning},
	booktitle = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
	year = {2021},
	abstract = {Open pit mines left many regions worldwide inhospitable or uninhabitable. Many sites are left behind in a hazardous or contaminated state, show remnants of waste, or have other restrictions imposed upon them, eg, for the protection of human or nature. Such information has to be permanently managed in order to reuse those areas in the future. In this work we present and evaluate an automated workflow for supporting the post-mining management of former lignite open pit mines in the eastern part of Germany, where prior to any planned land reuse, aforementioned information has to be acquired to ensure the safety and validity of such an endeavor. Usually, this information is found in expert reports, either in the form of paper documents, or in the best case as digitized unstructured text{\textemdash}all of them in German language. However, due to the size and complexity of these documents, any inquiry is tedious and time-consuming, thereby slowing down or even obstructing the reuse of related areas. Since no training data is available, we employ active learning in order to perform multi-label sentence classification for two categories of restrictions and seven categories of topics. The final system integrates optical character recognition (OCR), active-learning-based text classification, and geographic information system visualization in order to effectively extract, query, and visualize this information for any area of interest. Active learning and text classification results are twofold: Whereas the restriction categories were reasonably accurate (> 0.85 F1), the seven topic-oriented categories seemed to be complex even for human annotators and achieved {\textellipsis}},
	author = {Christopher Schr{\"o}der and Kim B{\"u}rgl and Yves Annanias and Andreas Niekler and Lydia M{\"u}lller and Daniel Wiegreffe and Christian Bender and Christoph Mengs and Gerik Scheuermann and Gerhard Heyer}
}
