Swiss Territorial Data Lab - STDL¶
The STDL aims to promote collective innovation around the Swiss territory and its digital copy. It mainly explores the possibilities provided by data science to improve official land registering.
A multidisciplinary team composed of cantonal, federal and academic partners is reinforced by engineers specialized in geographical data science to tackle the challenges around the management of territorial data-sets.
The developed STDL platform codes and documentation are published under open licenses to allow partners and Swiss territory management actors to leverage the developed technologies.
Exploratory Projects¶
Exploratory projects in the field of the Swiss territorial data are conducted at the demand of institutions or actors of the Swiss territory. The exploratory projects are conducted with the supervision of the principal in order to closely analyze the answers to the specifications along the project. The goal of exploratory project aims to provide proof-of-concept and expertise in the application of technologies to Swiss territorial data.
Green roofs: automatic detection of roof vegetation, vegetation type and covered surface from aerial imagery
November 2024
Clotilde Marmy (ExoLabs) - Ueli Mauch (Canton of Zürich) - Swann Destouches (Uzufly) - Alessandro Cerioni (Canton of Geneva) - Roxane Pott (swisstopo)
Proposed by the Canton of Zürich and Canton of Geneva - PROJ-VEGROOFS
With rising temperatures and increased rainfall, mapping green roofs is becoming important for urban planning in dense areas like Geneva, Zürich and the surrounding areas. Green roofs, whether engineered or spontaneous, provide cooling, rain capture, and habitats, supporting biodiversity. Using national aerial imagery and land survey data, the study focuses on identifying green roofs and distinguishing among various vegetation types, including extensive, intensive, spontaneous, lawn, and terrace categories. Machine learning and deep learning approaches have been developed to detect and classify green roofs in two study areas on the cantons of Geneva and Zürich. Regarding the machine learning setup, statistical descriptors for the roof occupancy were derived from airborne images to train a random forest and a logistic regression predicting if a roof was green or not. Metrics on the test dataset showed that the best performance was achieved by combining a random forest and logistic regression models, trained with pixel statistics from potential vegetated areas defined by NDVI and luminosity thresholds on the original images. This combination yielded a recall of 0.87 for the green class and an F1-score of 0.85. The approach leveraging a deep neural network for classification of the roofs in the six classes of the project is still in development.
Vectorization of historical cadastral plans from the 1850s in the Canton of Geneva
July 2024
Shanci Li (Uzufly) - Alessandro Cerioni (Canton of Geneva) - Clémence Herny (ExoLabs) - Henrich Duriaux (Canton of Geneva) - Roxane Pott (Swisstopo)
Proposed by the Canton of Geneva - PROJ-CADMAP
This project aims to vectorize historical cadastral plans using an innovative AI-driven pipeline. To overcome the complexities of plans manually-crafted by experts, the pipeline uses GIS software, computer vision algorithm and advanced deep learning techniques, such as deformable convolutional networks and vision transformers for automated map topology extraction and vectorization. The process includes removing background noise, deciphering symbols and improving vectorization accuracy using graph-based methods. An optical character recognition model extracts parcel indices and all information is combined in a spatially-referenced vector polygon format. Final vectorization yields a median Hausdorff distance of 3 pixels, while semantic classification of the detected polygons achieves an IoU of 0.98. Although most of the tasks are automated, minor manual corrections are still required to achieve satisfactory results. This semi-automated workflow saves at least 90% of the time required for fully manual vectorization of the entire historical plan. The vectorization of historical plans greatly facilitates the analysis of historical geographical data.
Detection of occupied and free surfaces on rooftops
May 2024
Clémence Herny (Exolabs) - Gwenaëlle Salamin (Exolabs) - Alessandro Cerioni (État de Genève) - Roxane Pott (swisstopo)
Proposed by the Canton of Geneva- PROJ-ROOFTOPS
Free roof surfaces offer great potential for the installation of new infrastructure, such as solar panels and vegetated rooftops. In this project, in collaboration with the Canton of Geneva, we have developed and tested three methods to automatically identify occupied and free surfaces on roofs: (1) classification of roof plane occupancy based on a random forest, (2) segmentation of objects in LiDAR point clouds based on a clustering and (3) segmentation of objects in aerial imagery based on a deep learning. The results are vector layers containing information about surface occupancy. The methods developed on a subset of 122 buildings achieved satisfactory performance. About 85% of the roof planes were correctly classified. The segmentation method was able to detect most of the objects with f1 scores of 0.78 and 0.75 for the LiDAR-based segmentation and the image-based segmentation respectively. The global shape of the occupied surface was more difficult to reproduce with a median intersection over the union of 0.35 and 0.37 respectively. The results of all three methods were considered satisfactory by the experts, with 70% to 95% of the results considered acceptable. Considering the quality of the results and the computational time, only the classification method was selected for application at the cantonal level.
Automatic Soil Segmentation
April 2024
Nicolas Beglinger (swisstopo) - Clotilde Marmy (ExoLabs) - Alessandro Cerioni (Canton of Geneva) - Roxane Pott (swisstopo)
Proposed by the Canton of Fribourg - PROJ-SOILS
This project focuses on developing an automated methodology to distinguish areas covered by pedological soil from areas comprised of non-soil. The goal is to generate high-resolution maps (10cm) to aid in the location and assessment of polluted soils. Towards this end, we utilize deep learning models to classify land cover types using raw, raster-based aerial imagery and digital elevation models (DEMs). Specifically, we assess models developed by the Institut National de l’Information Géographique et Forestière (IGN), the Haute Ecole d'Ingénierie et de Gestion du Canton de Vaud (HEIG-VD), and the Office Fédéral de la Statistique (OFS). The performance of the models is evaluated with the Matthew's correlation coefficient (MCC) and the Intersection over Union (IoU), as well as with qualitatifve assessments conducted by the beneficiaries of the project. In addition to testing pre-existing models, we fine-tuned the model developed by the HEIG-VD on a dataset specifically created for this project. The fine-tuning aimed to optimize the model performance on the specific use-case and to adapt it to the characteristics of the dataset: higher resolution imagery, different vegetation appearances due to seasonal differences, and a unique classification scheme. Fine-tuning with a mixed-resolution dataset improved the model performance of its application on lower-resolution imagery, which is proposed to be a solution to square artefacts that are common in inferences of attention-based models. Reaching an MCC score of 0.983, the findings demonstrate promising performance. The derived model produces satisfactory results, which have to be evaluated in a broader context before being published by the beneficiaries. Lastly, this report sheds light on potential improvements and highlights considerations for future work.
Cross-generational change detection in classified LiDAR point clouds for a semi-automated quality control
April 2024
Nicolas Münger (Uzufly) - Gwenaëlle Salamin (ExoLabs) - Alessandro Cerioni (Canton of Geneva) - Roxane Pott (swisstopo)
Proposed by the Federal Office of Topography swisstopo - PROJ-QALIDAR
The acquisition of LiDAR data has become standard practice at national and cantonal levels during the recent years in Switzerland. In 2024, swisstopo will complete a comprehensive campaign of 6 years covering the whole Swiss territory. The produced point clouds are classified post-acquisition, i.e. each point is attributed to a certain category, such as "building" or "vegetation". Despite the global control performed by providers, local inconsistencies in the classification persist. To ensure the quality of a Swiss-wide product, extensive time is invested by swisstopo in the control of the classification. This project aims to highlight changes in a new point cloud compared to a previous generation acting as reference. We propose here a method where a common grid is defined for the two generations of point clouds and their information is converted in voxels, summarizing the distribution of classes and comparable one-to-one. This method highlights zones of change by clustering the concerned voxels. Experts of the swisstopo LiDAR team declared themselves satisfied with the precision of the method.
Automatic detection and observation of mineral extraction sites in Switzerland
January 2024
Clémence Herny (ExoLabs) - Shanci Li (Uzufly) - Alessandro Cerioni (Etat de Genève) - Roxane Pott (Swisstopo)
Proposed by the Federal Office of Topography swisstopo - TASK-DQRY
The study of the evolution of mineral extraction sites (MES) is primordial for the management of mineral resources and the assessment of their environmental impact. In this context, swisstopo has solicited the STDL to automate the vectorisation of MES over the years. This tedious task was previously carried out manually and was not regularly updated. Automatic object detection using a deep learning method was applied to SWISSIMAGE RGB orthophotos with a spatial resolution of 1.6 m px-1. The trained model proved its ability to accurately detect MES, achieving a f1-score of 82%. Detection by inference was performed on images from 1999 to 2021, enabling us to track the evolution of potential MES over several years. Although the results are satisfactory, a careful examination of the detections must be carried out by experts to validate them as true MES. Despite this remaining manual work involved, the process is faster than a full manual vectorisation and can be used in the future to keep MES information up-to-date.
Dieback of beech trees: methodology for determining the health state of beech trees from airborne images and LiDAR point clouds
August 2023
Clotilde Marmy (ExoLabs) - Gwenaëlle Salamin (ExoLabs) - Alessandro Cerioni (Canton of Geneva) - Roxane Pott (swisstopo)
Proposed by the Republic and Canton of Jura - PROJ-HETRES
Beech trees are sensitive to drought and repeated episodes can cause dieback. This issue affects the Jura forests requiring the development of new tools for forest management. In this project, descriptors for the health state of beech trees were derived from LiDAR point clouds, airborne images and satellite images to train a random forest predicting the health state per tree in a study area (5 km²) in Ajoie. A map with three classes was produced: healthy, unhealthy, dead. Metrics computed on the test dataset revealed that the model trained with all the descriptors has an overall accuracy up to 0.79, as well as the model trained only with descriptors derived from airborne imagery. When all the descriptors are used, the yearly difference of NDVI between 2018 and 2019, the standard deviation of the blue band, the mean of the NIR band, the mean of the NDVI, the standard deviation of the canopy cover and the LiDAR reflectance appear to be important descriptors.
Using spatio-temporal neighbor data information to detect changes in land use and land cover
April 2023
Shanci Li (Uzufly) - Alessandro Cerioni (Canton of Geneva) - Clotilde Marmy (ExoLabs) - Roxane Pott (swisstopo)
Proposed by the Swiss Federal Statistical Office - PROJ-LANDSTATS
From 2020 on, the Swiss Federal Statistical Office started to update the land use/cover statistics over Switzerland for the fifth time. To help and lessen the heavy workload of the interpretation process, partially or fully automated approaches are being considered. The goal of this project was to evaluate the role of spatio-temporal neighbors in predicting class changes between two periods for each survey sample point. The methodolgy focused on change detection, by finding as many unchanged tiles as possible and miss as few changed tiles as possible. Logistic regression was used to assess the contribution of spatial and temporal neighbors to the change detection. While time deactivation and less-neighbors have a 0.2% decrease on the balanced accuracy, the space deactivation causes 1% decrease. Furthermore, XGBoost, random forest (RF), fully convolutional network (FCN) and recurrent convolutional neural network (RCNN) performance are compared by the means of a custom metric, established with the help of the interpretation team. For the spatial-temporal module, FCN outperforms all the models with a value of 0.259 for the custom metric, whereas the logistic regression indicates a custom metrics of 0.249. Then, FCN and RF are tested to combine the best performing model with the model trained by OFS on image data only. When using temporal-spatial neighors and image data as inputs, the final integration module achieves 0.438 in custom metric, against 0.374 when only the the image data is used.It was conclude that temporal-spatial neighbors showed that they could light the process of tile interpretation.
Classification of road surfaces
March 2023
Gwenaëlle Salamin (swisstopo) - Clémence Herny (Exolabs) - Roxane Pott (swisstopo) - Alessandro Cerioni (Canton of Geneva)
Proposed by the Federal Office of Topography swisstopo - PROJ-ROADSURF
The Swiss road network extends over 83’274 km. Information about the type of road surface is useful not only for the Swiss Federal Roads Office and engineering companies, but also for cyclists and hikers. Currently, the data creation and update is entirely done manually at the Swiss Federal Office of Topography. This is a time-consuming and methodical task, potentially suitable to automation by data science methods. The goal of this project is classifying Swiss roads according to their surface type, natural or artificial. We first searched for statistical differences between these two classes, in order to then perform supervised classification based on machine-learning methods. As we could not find any discriminant feature, we used deep learning methods.
Tree Detection from Point Clouds for the Canton of Geneva
March 2022
Alessandro Cerioni (Canton of Geneva) - Flann Chambers (University of Geneva) - Gilles Gay des Combes (CJBG - City of Geneva and University of Geneva) - Adrian Meyer (FHNW) - Roxane Pott (swisstopo)
Proposed by the Canton of Geneva - PROJ-TREEDET
Trees are essential assets, in urban context among others. Since several years, the Canton of Geneva maintains a digital inventory of isolated (or "urban") trees. This project aimed at designing a methodology to automatically update Geneva's tree inventory, using high-density LiDAR data and off-the-shelf software. Eventually, only the sub-task of detecting and geolocating trees was explored. Comparisons against ground truth data show that the task can be more or less tricky depending on how sparse or dense trees are. In mixed contexts, we managed to reach an accuracy of around 60%, which unfortunately is not high enough to foresee a fully unsupervised process. Still, as discussed in the concluding section there may be room for improvement.
Detection of thermal panels on canton territory to follow renewable energy deployment
February 2022
Nils Hamel (UNIGE) - Huriel Reichel (FHNW)
Project in collaboration with Geneva and Neuchâtel States - TASK-TPNL
Deployment of renewable energy becomes a major stake in front of our societies challenges. This imposes authorities and domain expert to promote and to demonstrate the deployment of such energetic solutions. In case of thermal panels, politics ask domain expert to certify, along the year, of the amount of deployed surface. In front of such challenge, this project aims to determine to which extent data science can ease the survey of thermal panel installations deployment and how the work of domain expert can be eased.
Automatic detection of quarries and the lithology below them in Switzerland
January 2022
Huriel Reichel (FHNW) - Nils Hamel (UNIGE)
Proposed by the Federal Office of Topography swisstopo - TASK-DQRY
Mining is an important economic activity in Switzerland and therefore it is monitored by the Confederation through swisstopo. To this points, the identification of quarries has been mode manually, which even being done with very high quality, unfortunately does not follow the constant changing and updating pattern of these features. For this reason, swisstopo contacted the STDL to automatically detect quarries through the whole country. The training was done using SWISSIMAGE with 10cm spatial resolution and the Deep Learning Framework from the STDL. Moreover there were two iteration steps with the domain expert which included the manual correction of detection for new training. Interaction with the domain expert was very relevant for final results and summing to his appreciation, an f1-score of 85% was obtained in the end, which due to peculiar characteristics of quarries can be considered an optimal result.
Updating the «Cultivable Area» Layer of the Agricultural Office, Canton of Thurgau
June 2021
Adrian Meyer (FHNW) - Pascal Salathé (FHNW)
Proposed by the Canton of Thurgau - PROJ-TGLN
The Cultivable agricultural area layer ("LN, Landwirtschaftliche Nutzfläche") is a GIS vector product maintained by the cantonal agricultural offices and serves as the key calculation index for the receipt of direct subsidy contributions to farms. The canton of Thurgau requested a spatial vector layer indicating locations and area consumption extent of the largest silage bale deposits intersecting with the known LN area, since areas used for silage bale storage are not eligible for subsidies. Having detections of such objects readily available greatly reduces the workload of the responsible official by directing the monitoring process to the relevant hotspots. Ultimately public economical damage can be prevented which would result from the payout of unjustified subsidy contributions.
Swimming Pool Detection for the Canton of Thurgau
April 2021
Adrian Meyer (FHNW) - Alessandro Cerioni (Canton of Geneva)
Proposed by the Canton of Thurgau - PROJ-TGPOOL
The Canton of Thurgau entrusted the STDL with the task of producing swimming pool detections over the cantonal area. Specifically interesting was to leverage the ground truth annotation data from the Canton of Geneva to generate a predictive model in Thurgau while using the publicly available SWISSIMAGE aerial imagery datasets provided by swisstopo. The STDL object detection framework produced highly accurate predictions of swimming pools in Thurgau and thereby proved transferability from one canton to another without having to manually redigitize annotations. These promising detections showcase the highly useful potential of this approach by greatly reducing the need of repetitive manual labour.
Completion of the federal register of buildings and dwellings
February 2021
Nils Hamel (UNIGE) - Huriel Reichel (swisstopo)
Proposed by the Federal Statistical Office - TASK-REGBL
The Swiss Federal Statistical Office is in charge of the national Register of of Buildings and Dwellings (RBD) which keep tracks of every existing building in Switzerland. Currently, the register is being completed with building in addition to regular dwellings to offer a reliable and official source of information. The completion of the register introduced issue dues to missing information and their difficulty to be collected. The construction years of the building is one missing information for large amount of register entries. The Statistical Office mandated the STDL to investigate on the possibility to use the Swiss National Maps to extract this missing information using an automated process. A research was conducted in this direction with the development of a proof-of-concept and a reliable methodology to assess the obtained results.
Swimming Pool Detection from Aerial Images over the Canton of Geneva
January 2021
Alessandro Cerioni (Canton of Geneva) - Adrian Meyer (FHNW)
Proposed by the Canton of Geneva - PROJ-GEPOOL
Object detection is one of the computer vision tasks which can benefit from Deep Learning methods. The STDL team managed to leverage state-of-art methods and already existing open datasets to first build a swimming pool detector, then to use it to potentially detect unregistered swimming pools over the Canton of Geneva. Despite the success of our approach, we will argue that domain expertise still remains key to post-process detections in order to tell objects which are subject to registration from those which aren't. Pairing semi-automatic Deep Learning methods with domain expertise turns out to pave the way to novel workflows allowing administrations to keep cadastral information up to date.
Difference models applied to the land register
November 2020
Nils Hamel (UNIGE) - Huriel Reichel (swisstopo)
Project scheduled in the STDL research roadmap - TASK-DTRK
Being able to track modifications in the evolution of geographical datasets is one important aspect in territory management, as a large amount of information can be extracted out of differences models. Differences detection can also be a tool used to assess the evolution of a geographical model through time. In this research project, we apply differences detection on INTERLIS models of the official Swiss land registers in order to emphasize and follow its evolution and to demonstrate that change in reference frames can be detected and assessed.
Research Developments¶
Research developments are conducted aside of the research projects to provide a framework of tools and expertise around the Swiss territorial data and related technologies. The research developments are conducted according to the research plan established by the data scientists and validated by the steering committee.
OBJECT DETECTION FRAMEWORK
November 2021
**Alessandro Cerioni (Canton of Geneva) - Clémence Herny (Exolabs) - Adrian Meyer (FHNW) - Gwenaëlle Salamin (Exolabs) **
Project scheduled in the STDL research roadmap - TASK-IDET
This strategic component of the STDL consists of the automated analysis of geospatial images using deep learning while providing practical applications for specific use cases. The overall goal is the extraction of vectorized semantic information from remote sensing data. The involved case studies revolve around concrete object detection use cases deploying modern machine learning methods and utilizing a multitude of available datasets. The goal is to arrive at a prototypical platform for object detection which is highly useful not only for cadastre specialists and authorities but also for stakeholders at various contact points in society.
AUTOMATIC DETECTION OF CHANGES IN THE ENVIRONMENT
November 2020
Nils Hamel (UNIGE)
Project scheduled in the STDL research roadmap - TASK-DIFF
Developed at EPFL with the collaboration of Cadastre Suisse to handle large scale geographical models of different nature, the STDL 4D platform offers a robust and efficient indexation methodology allowing to manage storage and access to large-scale models. In addition to spatial indexation, the platform also includes time as part of the indexation, allowing any area to be described by models in both spatial and temporal dimensions. In this development project, the notion of model temporal derivative is explored and proof-of-concepts are implemented in the platform. The goal is to demonstrate that, in addition to their formal content, models coming with different temporal versions can be derived along the time dimension to compute difference models. Such proof-of-concept is developed for both point cloud and vectorial models, demonstrating that the indexation formalism of the platform is able to ease considerably the computation of difference models. This research project demonstrates that the time dimension can be fully exploited in order to access the data it holds.
Steering Committee¶
The steering committee of the Swiss Territorial Data Lab is composed of Swiss public administrations bringing their expertise and competences to guide the conducted projects and developments.
Members of the STDL steering committee
Submitting a project¶
To submit a project to the STDL, simply fill this form. To contact the STDL, please write an email to info@stdl.ch. We will reply as soon as possible!