add link to civils.ai in related work

README.md

@@ -43,7 +43,7 @@ Below is a list of the most relevant properties for the extraction of structure
 
 ### Related work
 
-Existing work related to this project is mostly focussed on the extraction and classification of specific properties from textual geological descriptions. Notable examples include [GEOBERTje](https://www.arxiv.org/abs/2407.10991) (Belgium), [geo-ner-model](https://github.com/BritishGeologicalSurvey/geo-ner-model) (UK), [GeoVec](https://www.sciencedirect.com/science/article/pii/S0098300419306533) und [dh2loop](https://github.com/Loop3D/dh2loop) (Australia). The scope of this project is considerable wider, in particular regarding the goal of understanding borehole profiles in various languages and with an unknown layout, where the document structure first needs to be understood, before the relevant text fragments can be identified and extracted. A commercial solution with similar goals is [Civils.ai](https://civils.ai/) ([Youtube video](https://www.youtube.com/watch?v=WkAttZWbjBk)).
+Existing work related to this project is mostly focussed on the extraction and classification of specific properties from textual geological descriptions. Notable examples include [GEOBERTje](https://www.arxiv.org/abs/2407.10991) (Belgium), [geo-ner-model](https://github.com/BritishGeologicalSurvey/geo-ner-model) (UK), [GeoVec](https://www.sciencedirect.com/science/article/pii/S0098300419306533) und [dh2loop](https://github.com/Loop3D/dh2loop) (Australia). The scope of this project is considerable wider, in particular regarding the goal of understanding borehole profiles in various languages and with an unknown layout, where the document structure first needs to be understood, before the relevant text fragments can be identified and extracted. A commercial solution with similar goals is [Civils.ai](https://civils.ai/geotechnical-engineering-ai-automation) ([Youtube video](https://www.youtube.com/watch?v=WkAttZWbjBk)).
 
 The automatic data extraction pipeline can be considered to belong to the field of [automatic/intelligent document processing](https://en.wikipedia.org/wiki/Document_processing). As such, it involves a combination of methods from multiple fields in data science and machine learning, in particular computer vision (e.g. object detection, line detection) and natural language processing (large language models, named entity recognition). Some of these have already been implemented (e.g. the [Line Segment Detector](https://docs.opencv.org/3.4/db/d73/classcv_1_1LineSegmentDetector.html) algorithm), others are planned as future work.