Supporting Land Reuse of Former Open Pit Mining Sites using Text Classification and Active Learning (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, e.g., 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—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 mediocre evaluation scores (<0.70 F1).

## introduction in many parts of the world, raw materials were mined in open pit mines during the last century, leaving many of these regions inhospitable or uninhabitable. to put these regions back into use, entire stretches of land must be renaturalized, which means that land must be ecologically restored with the aim to ultimately increase biodiversity, or recultivated, which means its productivity must be restored, e.g., reused for agriculture, recreational areas, industrial parks, solar and wind farms, or as building land @xcite . in the following, we subsume both renaturalization and recultivation under land reuse. for land reuse, it is essential that all relevant information about the sites is retained, which used to be recorded in the form of textual reports. such reports include information such as, among others, hazards, soil composition, or environmental factors. therefore, having access to all these reports, it can be determined if a site can be reused immediately, only under certain conditions, or not at all in the foreseeable future. for reaching a sustainable future, the united nations (2015) has defined objectives, called sustainable development goals (sdgs). land reuse is a shared common denominator among several of those goals such as "zero hunger", "clean water and sanitation", "sustainable cities and communities", "climate action", "life below water", and "life on land". moreover, it provides co-benefit to all sdgs as shown by @xcite and directly supports "life on land". by implication, anything that obstructs land reuse also impedes the fulfillment of several sdgs. this work deals with the real-world use case of post-mining management @xcite of former lignite open pit mines in the eastern part of germany. here, a large number of such documents exist, and moreover, there is metadata maintained, which maps each document to its related area. apart from that, before any land can be reused in these areas, it is legally required that local authorities must be consulted before proceeding any further. this process includes seeing through numerous legacy documents, which is laborious, timeconsuming and delays a subsequent reuse of such areas. we address this issue by demonstrating and evaluating a workflow consisting of optical character recognition (ocr), text classification and active learning, whose results are then visualized by a geographic information system (gis). by automating information extraction and making extracted results available through a gis, we increase efficiency by which information about a specific location of interest can be queried. this can accelerate the reuse of land by supporting the efficiency of employees managing these areas, and thereby contributes towards the fulfillment of several sdgs. this necessary review of a multitude of documents, which is obligatory prior to any land reuse, is aggravated even more by germany's federal structure @xcite due to which land management is a task of the municipalities. the federal government, as well as the states are responsible for the sdgs' implementation, which is then passed on to the municipalities, which therefore are effectively responsible for supporting sdgs. municipalities, however, do not have a standardized software infrastructure @xcite , which results in a heterogeneous data management landscape and thereby makes the implementation of sdgs challenging, especially for small municipalities. information about former lignite open pit mines is stored in independent gises, related unstructured documents are stored in dedicated storage systems (either in form of piles of paper, scanned documents, or even as digitized text), and the connections between documents and geographic coordinates are stored in yet other databases. in order to obtain information about an area of interest, all information must be contextualized, compiled, and manually evaluated. although the presented approach is tailored towards the post-mining management in eastern germany, this is relevant to many other countries in the world, which are also concerned with stopping lignite and coal mining to reduce co 2 emissions. to give a few examples, belgium performed coal phase-out in 2016, sweden and austria in 2020; canada will follow in 2030, and germany in 2038. all of these countries will need to post-manage former mining sites in order to reuse the affected areas. apart from lignite and coal mining, this is also true for other mining sites. once resources are exhausted or are no longer needed, land has to be renaturalized or recultivated or will stay deserted for unknown time. there are a variety of ocr engines available, with tesseract (smith, 1987) being a good starting point. tesseract offers a number of pre-processing mechanisms for document images, however, it does not implement the full range of state-of-the-art ocr. image pre-processing as proposed and implemented by the ocr-d project @xcite , is beneficial to additionally extend the tool with the latest developments in ocr. in recent years, text classification, like many other fields in natural language processing, has experienced a paradigm shift towards transformerbased models @xcite , which raised the state-of-the-art results on many tasks. besides the impressive performance gains, the main advantage of using a pre-trained model is that its performance can be translated to low-data scenarios, which were previously challenging due to deep models overfitting on small data. transformers, however, have been shown to work well on small data @xcite , and consequently open up new possibilities on previously challenging tasks. geographic information systems are a common technological choice to visualize spatial data on cartographic maps and have been shown to be invaluable for supporting sdgs @xcite . using a gis, one can combine a database storing textual information with the spatial data to support experts in the decision-making process or to enable the exploration of data. to support renaturalization of a river valley, @xcite used a gis to analyze hydrological aspects in the area and develop a plan for renaturalization. similarly to our work, they also combined several layers of features in the gis. a recent toolbox of the commercial arcgis software called locatext (esri) can connect a larger number of unstructured datasets into a running gis, however, although it can automatically link information and coordinates from the data, it does not support the extraction and processing of unstructured information and other attributes providing further information. ## data we use data from the lausitzer und mitteldeutsche bergbauverwaltungsgesellschaft mbh (lmbv) foot_0 , who are responsible for the management and reuse of abandoned mining sites in the eastern part of germany. for this purpose, they archive and manage all documents related to sites in this area, and issue new documents if required. moreover, they are obligated to provide reliable information about the managed lands for the public on request. such requests require, among others, to inform about any restrictions for the specific area, which can be found in the associated documents. an illustrated example is shown in figure 1 . for research, the lmbv provided us with 31,605 of such documents (16,883 for the region lausitz foot_1 , 14,722 for the region mitteldeutschland foot_2 ). the oldest documents date back to the 1960s, but scans were only produced within the last 20 years. the documents encompass several different types, for example, reports, drilling logs, expert opinions, statements, plans, maps, and correspondences. the quality of the scans varies from excellent to fair quality. moreover, some documents are stored in other digital formats (.doc, .docx, .odf) and others are stored as scanned images. the documents have different origins: they are authored by the companies mining the open pit mine, by the lmbv managing the closed open pit mines, by companies responsible for certain subtasks such as building infrastructure, or by other experts. they include documents from the time when open pit mines were actively mined but also documents created after the mines were closed. besides the documents, our dataset contains over 30,000 geographic features. these features are described as points, lines, polygons and multipolygons, and can be visualized in a gis. in addition, these data are provided with additional non-spatial information, such as the geographical affiliation of the documents mentioned. ## approach the goal of our approach is to detect restriction and topic labels at the sentence level. subsequently, we can map predicted labels to geospatial data, which is already available in a structured format. this means, with a process chain of ocr, text classification, and gis, we can effectively detect the presence or absence of labels at geographic coordinates of interest. in the end, this can be directly used to manage land reuse efforts, thereby supporting aforementioned sdgs. as existing ocr solutions are tried and tested, and the geospatial link is already given, the main challenge of this method is text classification, namely: (1) there is no predefined industrial standard for the labels which are not formally defined but given by some exemplary formulations and keywords provided by the lmbv. consequently, the definitions are incomplete and new formulation not using the keywords are expected. (2) the documents exhibit a domain-specific, often convoluted, vocabulary. ## experiments we evaluate multi-label active learning performed by three human annotators, who each train a sentence classification model for classifying restrictions and topics, resulting in two runs per person. ## visualization and interaction use case as an example, we present a workflow regarding areas which may not be entered during heavy rains for safety reasons. to answer a request (see section 3), which e.g., is asking if a specific area may be entered, the expert uses the gis, centers the map on the corresponding area, and displays the associated features (e.g., active dismantling areas, see figure 2 a ). to enable the expert to analyze the different feature categories, the displayed features are colored by category as suggested by @xcite . since areas can overlap in the map display, all features are colored only semi-transparently. information immediately prohibiting certain activities is identified by clicking on a feature, which displays the non-spatial data in an information panel (figure 2 a-b ). to keep the expert's overview of the selected features, they are represented with a striped texture. all restrictions that result from the documents linked to the selected feature are listed (figure 2 c ). the entries are grouped by the restriction type and sorted by a confidence value (figure 2 c 1 and c 2 ). the document title and the sentence from which the restriction is derived are indicated. a click on the title opens a new window for reading the document. this list provides the expert with direct feedback on which documents might be relevant and, without reading them completely, an overview of which usage restrictions are present. this information is crucial for the experts, as it can have a significant impact on planned projects and their planning time. additionally, the area described by a document can be superimposed with weather data. in this way, decisions regarding conditional restrictions (e.g., "may not enter shore areas during heavy rain") can also be made more quickly and directly on the basis of the system. the selected features overlap with a heavy rain area represented by isobands, therefore the request is directly answered and access to the area is currently prohibited (figure 2 a ). however, for other restrictions, more information may be necessary, because experts often compare the region of interest with similar regions. therefore, we provide a filter to highlight all features within the same restriction topic (figure 2 c). by analyzing similar regions, the expert can derive recommendations for action, which might be necessary for the land reuse of an area. recommendations for possible usage restrictions can also be derived in this way. furthermore, this comparison can prevent actions from not being taken or from being taken too late, because the current information does not make them appear necessary, but it is clear from similar projects that they may nevertheless become necessary. this leads to a safe and quick reuse of regions maintained in that manner since precautions can be taken in advance. ## conclusions and future work in this work, we have presented and evaluated a system which automates information requests related to the post-management of former open pit mines by leveraging unstructured and geospatial data. we used active learning for multi-label text classification to extract restrictions and topics from unstructured text in legacy documents and visualized the results using a gis. as a result, targeted queries about restrictions and topics at specific geographic locations can be obtained much more efficiently, thereby speeding up the process of land reuse, which directly contributes to several sdgs. further research is needed to shift recall towards 100% to minimize false negatives, then correcting false positives in the system.