Supporting Land Reuse of Former Open Pit Mining Sites using Text Classification and Active Learning

TL;DR

This paper presents an integrated approach combining OCR, text classification, active learning, and GIS visualization to efficiently analyze unstructured expert reports for land reuse of former open pit mining sites, supporting sustainable development.

Contribution

It introduces a novel pipeline that leverages active learning to classify and extract geographic information from unstructured reports without initial training data.

Findings

Restrictions categories achieved over 0.85 F1 score.

Topic-oriented categories achieved below 0.70 F1 score.

Active learning effectively reduces the need for labeled data.

Abstract

Open pit mines left many regions worldwide inhospitable or uninhabitable. To put these regions back into use, entire stretches of land must be renaturalized. For the sustainable subsequent use or transfer to a new primary use, many contaminated sites and soil information have to be permanently managed. In most cases, this information is available in the form of expert reports in unstructured data collections or file folders, which in the best case are digitized. Due to size and complexity of the data, it is difficult for a single person to have an overview of this data in order to be able to make reliable statements. This is one of the most important obstacles to the rapid transfer of these areas to after-use. An information-based approach to this issue supports fulfilling several Sustainable Development Goals regarding environment issues, health and climate action. We use a stack of Optical Character Recognition, Text Classification, Active Learning and Geographic Information System Visualization to effectively mine and visualize this information. Subsequently, we link the extracted information to geographic coordinates and visualize them using a Geographic Information System. Active Learning plays a vital role because our dataset provides no training data. In total, we process nine categories and actively learn their representation in our dataset. We evaluate the OCR, Active Learning and Text Classification separately to report the performance of the system. Active Learning and text classification results are twofold: Whereas our categories about restrictions work sufficient ($>$.85 F1), the seven topic-oriented categories were complicated for human coders and hence the results achieved mediocre evaluation scores ($<$.70 F1).