Tag: Spatial Data Analytics

Workshop – From Crowdsourced to Reliable Data: AI Quality Evaluation of OpenStreetMap

Written by dsa_admin on 24. September 2024.

OpenStreetMap (OSM) is a valuable crowdsourced alternative to commercial and governmental geospatial data providers, with proven applications e.g. in the environment, tourism and emergency services. However, the lack of quality assessment tools hinders its wider adoption. This workshop aims to address the need for reliable and standardized data quality evaluation in OSM, with a focus on AI-based methods.

Current research prototypes such as ‘OSM Data Completeness’ and ‘ohsome quality analyst’ offer intrinsic solutions for data quality evaluation in specific regions based on user requirements. But there’s potential for improvement in usability and integration of extrinsic approaches.

The workshop will explore how these tools can be developed for production use and identify areas for further research. By discussing data quality evaluation solutions, the workshop aims to enable the establishment of an applied research and development agenda to better evaluate the suitability of OSM data.

The workshop will be held in German and English.

Registration and contact: Mail to stefan.keller@ost.ch

GEOSummit – Session on New Methods in Spatial Data Analytics

Written by dsa_admin on 22. March 2024.

With the increasing availability of spatial data, analysis methods are also evolving. It is therefore not surprising that, in addition to established approaches from the field of machine learning, Large Language Models, which are very present in the media, are also used when working with spatial data. But are these sometimes very complex methods only to be found in research or are there already concrete applications and operational services in Switzerland? Examples from research and practice are presented, which are convincing with new methods of spatial data analysis.

Find more information on the official website.

Spatial Data Analytics – AI-driven Probability Maps

Written by dsa_admin on 15. March 2024.

In the field of environmental monitoring, artificial intelligence (AI) coupled with probability maps emerges as a powerful tool for comprehensively understanding and managing ecological systems. By harnessing machine learning algorithms, intricate patterns within environmental datasets can be discerned with unprecedented accuracy. Based on this understanding, probability maps can be calculated to offer valuable insights into the likelihood of various environmental events. These maps serve as crucial decision-making aids for policymakers, conservationists, and researchers alike, enabling proactive measures to mitigate ecological threats and promote sustainable practices.

Schedule:

14:45 – 14:55 – Intro and introductions (Dr. László István Etesi – FHNW)
14:55 – 15:30 – Probability Maps: Current research & applications in the field of environmental monitoring
15:30 – 16:00 – Break
16:00 – 16:30 – Indicate challenges around robust multi-sensor & multi-scale data integration and harmonization
16:30 – 17:00 – Discussion on ideas which can be further evaluated in the frame of the Innovation Booster