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STDL Object Detection Framework

Adrian F. Meyer, FHNW

Alessandro Cerioni, Etat de Geneve

Current Mandate Schedule : October 2020 to September 2021 (with extension options until 2024)

Objective

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 semantic information from remote sensing data. The earlier involved case studies revolve around concrete object detection use cases deploying modern machine learning methods and utilizing a multitude of available datasets. Later, full semantic surface layers can be produced leveraging the obtained datasets to arrive at a prototypical platform for object detection which is highly useful for decision makers at various contact points in society.


Generating a Model from Cadastral Vectors and Aerial Images to Predict Objects in the Same or a New Area of Interest (AoI).

Background and Potential Use Cases

Swimming Pools

Providing a reliable detection of swimming pools allows authorities to assess the status quo to update archival datasets and to reinforce administrative construction permit processes. The status quo is based on manually digitized cadastral information. This data is used to extract feature masks which can be applied to orthophoto imagery such as the SWISSIMAGE dataset or aerial photos provided by the end users. Deep Learning algorithms such as Faster RCNN or Mask RCNN then allow the detection of previously unregistered swimming pools in a defined perimeter. Achievable detection accuracies range above 90% (F1 Score). Current users of the technology include the Canton of Geneva and the Canton of Neuchâtel.


Example Detections of Swimming Pools Missing in the Cadastre.

Thermal Panels


Thermal Solar Panels Can Contribute a Substantial Amount to Reducing Carbon Emissions.

Silage Hay Bales


Locations of Silage Hay Bales are Relevant for the Calculation of Governmental Agriculture Subsidies.

Methodology

Area of Interest Preparation


Slippy Map Tiles

Tiling Subsystem

WMS and MIL Support


Slippy Map Tiles

Detectron2 Core

Dataset Splitting


Subdivision of Ground Truth Datasets

Transfer Learning


Subdivision of Ground Truth Datasets

Hyperparameter Tuning

Parallelisation

High-Performance Computing Cluster at FHNW


HPE Apollo 6500 with 4 NVidia V100 GPUs

Joblib Backend

Datasets

Ground Truth Labels

Swimming Pools of Geneva and Neuchâtel (partial)


Label inputs for deep learning derived from cadastral data


Approximately 3000 cross checked swimming pool annotations are available as vectorized shapes in the Cantons of Geneva and partially in Neuchâtel. They are compatible with orthophotos from 2018/19 such as the latest SWISSIMAGE 10cm layer.

Thermal Panels in Northwestern Switzerland

The thermal panel dataset of the STDL is based on a predecessor project. The project «SolAi» was launched in 2018 at the Institute of Geomatics (IGEO) of the University of Applied Sciences Northwestern Switzerland (FHNW), in collaboration with the Swiss Federal Office of Energy (SFOE) and will be finished by the end of 2020. The project aims to use Mask R-CNN algorithms to automatically identify and quantify existing solar installations from Swissimage orthophotos. Such an approach should then serve as a basis for the implementation of the energy strategy and statistical estimation models of the solar market. A solar register is already available in Switzerland based on applications of government subsidies. This dataset is lacking integral completeness as well as absolute shape and positions of the installations though. To date, Switzerland still lacks reliable position and area data of the photovoltaic and solar thermal systems already installed in order to enable a complete evaluation in conjunction with the solar cadastre data. In the scope of the project over 30'000 polygons of solar panels classified into "Photovoltaic" and "Thermal" installations were drawn over the SWISSIMAGE dataset to generate traning data, currently yielding a mean average prediction accuracy of ~87%.


The Predecessor-Project «SolAi» was funded by the Swiss Federal Office of Energy

Various cantonal and federal authorities as well as research groups have shown exceptional interest in obtaining the dataset and building on these scientific findings. The use case will therefore be continued in the framework of the Swiss Territorial Data Lab project to refine the scope of the outcomes to end user needs and achieve a maturation of the classification results through hyperparameter optimization, retraining on multispectral imagery and evaluating prediction/inferencing robustness in different radiometric scenarios.


Early Detection Result of a Resnet-50 Mask-RCNN architecture

Silage Hay Bales in Thurgau

700 Labels as Vectors.

Data Sources

  • SWISSIMAGE RGB 10cm by swisstopo

  • SWISSIMAGE RS 10cm by swisstopo

  • Labels drawn from the official Swiss cadastral services. The Swiss cadastral system comprises the cadastral surveying, the Cadastre of Public-law Restrictions on landownership (PLR-cadastre) and the land register.

  • SITG - Geneva Geodata Services

  • SITN - Neuchâtel Geodata Services

Results

Metrics Output


Example Detections of Swimming Pools Missing in the Cadastre.

Swimming Pools


Zoom Level Dependency


Detected Swimming Pools in the Canton of Neuchâtel

Silage Hay Bales


Early Detection Result of a Resnet-50 Mask-RCNN architecture

Outlook

Ultimately, an automatized system for surface classification based on aerial imagery and additional data sources will be proposed that allows consistent differential surface segmentation as a basis for differential change analysis. Spatiotemporal data storage provides insight into current, historical and future territorial features scalabe from small communal objects to the environmental and landscape levels.

  • Institute Geomatics FHNW

    • Project Area Statistics
    • Project Sol Ai
    • Project Animal Detection
  • Swisstopo

  • BFS / OFS

  • BFE / Energiestrategie 2050