Offre proposée par
Intership | Automatic 3D Vectorisation of Historical Mining Maps with Deep Learn
After decades of coal exploitation during the early 20th century in Limburg, lagging aftereffects at the surface, such as local sinkholes, are still experienced today. Additionally regional surface uplift has also been observed by satellite geodesy (InSAR), which has been attributed to the cessation of mine water pumping after the end of coal exploitation.Many efforts have been made to understand how sinkholes are formed and to be able to predict where they are going to happen. Similar efforts have been used to understand the regional uplift signal and small faults at the surface, but a proper modelling of these processes requires a well-known geometry and location of the mined areas.In 2015 TNO published tens of thousands of georeferenced historical maps of the mining activities of Limburg. These historical mining maps indicate where the coal was mined, the pathways from which the coal was transported and many other important features that could provide a good indication of vulnerable locations from a subsurface mining configuration perspective.While these historical maps hold important information of the subsurface, the fact that they are in raster and not in vector format makes them heavy and difficult to visualize and use for subsurface modelling. Additionally, these maps hold important information (e.g. handwritten values) which would be good to retain together with the vectorised information.
What will be your role?
You will be training an existing data set to extract relevant information (vector, text and numeric) from historical maps with Deep Neural Network algorithms. You will be using previously vectorized maps as training datasets, as well as performing your own training dataset using neural network. You will be exploring data augmentation tools and analyze how these technologies lead to a 3D automatized vectorization product. You will be working within a team that will help you to interpret the relevant features to be extracted from the maps, and you will be in contact with stakeholders that will use your final product. Your final product will be contributing to a newly 3D vectorized database of the Limburg mining areas, shafts and galleries which will serve to support research and decision making.
How do you want to contribute to tomorrow's world? How big can your impact be? Come and work at TNO and envision it.
What we expect from you
Either a computer science student with an interest in geosciences, or a geoscientist with knowledge in the computer science field. Any other field with a strong image processing component (e.g. Remote sensing), mathematics or engineering with affinity for AI and training Deep Learning networks and a passion for applied Earth sciences.Language: English required, Dutch optional but desirableInterests: Deep Learning, Geology, image processingSkills: quick in grasping new concepts and in identifying the most crucial aspects of a complex problem involving different expertise. Good math, statistical, and computing skills. Skilled in Python, MATLAB, or similar tools.
What you'll get in return
You want to work on the precursor of your career; a work placement gives you an opportunity to take a good look at your prospective future employer. TNO goes a step further. It's not just looking that interests us; you and your knowledge are essential to our innovation. That's why we attach a great deal of value to your personal and professional development. You will, of course, be properly supervised during your work placement and be given the scope for you to get the best out of yourself. Naturally, we provide suitable work placement compensation.
TNO as an employer
At TNO, we innovate for a healthier, safer and more sustainable life. And for a strong economy. Since 1932, we have been making knowledge and technology available for the common good. We find each other in wonder and ingenuity. We are driven to push boundaries. There is all the space and support for your talent and ambition. You work with people who will challenge you: who inspire you and want to learn from you. Our state-of-the-art facilities are there to realize your vision. What you do at TNO matters: impact makes the difference. Because with every innovation you contribute to tomorrow's world. Read more about TNO as an employer.
The selection process
After the first CV selection, the application process will be conducted by the concerning department. TNO will provide a suitable internship agreement. If you have any questions about this vacancy, you can contact the contact person mentioned below.
Due to Covid-19 and the consequent uncertainties and restrictions, students who are not residing in the Netherlands may currently not be able to start an internship or graduation project at TNO.
Has this job opening sparked your interest?
Then we'd like to hear from you! Please contact us for more information about the job or the selection process. To apply, please upload your CV and covering letter using the 'apply now' button.
Contact: Joana Esteves Martins
Phone number: +31611516694
Note that applications via email and third party applications are not taken into consideration.