BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//pretalx//talks.staging.osgeo.org//foss4g-europe-2025//talk//8NPSR
 Z
BEGIN:VTIMEZONE
TZID:CET
BEGIN:STANDARD
DTSTART:20001029T040000
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10
TZNAME:CET
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
END:STANDARD
BEGIN:DAYLIGHT
DTSTART:20000326T030000
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3
TZNAME:CEST
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
END:DAYLIGHT
END:VTIMEZONE
BEGIN:VEVENT
UID:pretalx-foss4g-europe-2025-8NPSRZ@talks.staging.osgeo.org
DTSTART;TZID=CET:20250716T113000
DTEND;TZID=CET:20250716T120000
DESCRIPTION:Monitoring the movement of people\, animals\, and vehicles in d
 aily territorial use can significantly improve spatial development\, enhan
 cing safety\, sustainability\, and inclusiveness. Across Ticino canton\, m
 any stakeholders\, such as regional natural parks\, are eager for a system
  that can provide valuable data on space usage to better manage costs\, ju
 stify new investments\, and handle maintenance activities [1\, 2]. \n\nDur
 ing the INSUBRIPARKS Interreg project\, a cost-effective prototype was dev
 eloped to track and count the passage of tourists in specific park areas. 
 The system consists of a device with a camera that\, through image recogni
 tion and machine learning techniques\, collects data that are then sent to
  a data warehouse based on istSOS [3]\, an open-source implementation of t
 he Sensor Observation Service (SOS) standard of the Open Geospatial Consor
 tium (OGC). The system fully complies with European GDPR regulations\, as 
 it only stores anonymous metadata such as the type of object (person\, car
 \, bicycle\, etc.) and the object's movement path. No video or images capt
 ured by the camera are saved. \n\nThanks to these features\, the device ha
 s been adopted also in the Adaptive Space project\, funded by the Federal 
 Office of the Spatial Development (ARE). This project aims to develop a pr
 otocol with guidelines for the inclusive planning of last-mile mobility. \
 n\nTo this end\, two sites were selected as study areas (SA) to analyse th
 e behaviour of citizens who frequently use these spaces. One is located ou
 tside the Mendrisio railway station (SA1). This area is occupied by four p
 arking lots and is subject to movements that prioritize pedestrian passage
  and vehicle flow to and from the station and the city centre. The second 
 site is located at Mendrisio S. Martino\, also outside the railway station
  (SA2). This area is of particular interest because new structures have be
 en built over the past year\, impacting pedestrian use due to an increase 
 in traffic from both vehicles and people. In fact\, this area is commonly 
 used as a passageway for people heading to the industrial zone. \n\nThe me
 thodology involved an automatic detection approach by installing sensors t
 o collect continuous data. Three main data collection campaigns were condu
 cted at each site: one in summer\, one in autumn\, and one in winter. Sinc
 e the device has high power consumption\, it had to be installed with a ba
 ttery\, as no viable solution was found to connect the sensor to a continu
 ous power source. During the campaigns\, the device collected data on the 
 number of detected objects\, their classification\, and their movements ac
 ross the monitored areas\, using tracking capabilities that gather coordin
 ates frame by frame to monitor the movement of each object. Such data have
  been validated through manual sampling and\, on the other hand\, have bee
 n provided a broader overview of the usage of the selected areas across di
 fferent periods of the year.  \n\nThe analysis developed during this proje
 ct focused on tracking data coordinates\, which proved to be essential for
  understanding how the objects are distributed across the area and determi
 ning where activities are most concentrated\, based on the different categ
 ories to which each object belongs. This approach results in the generatio
 n of heatmaps for pedestrians and vehicles using data from the entire day\
 , as well as filtering for evening and morning peak-hour traffic. The data
 set has also been evaluated in terms of data accuracy\, as for each object
  present in the frame\, the percent of confidence is archived. By plotting
  this data through a histogram\, it was possible to understand the accurac
 y assessments of the detected objects from the chosen classification model
 . \n\nFurthermore\, two different analytical methods were applied to the t
 wo study areas. In SA1\, alongside heatmap generation and accuracy evaluat
 ion\, the analysis focused on parking areas by calculating the stationary 
 time of detected objects\, which helped to assess how these parking areas 
 are utilized by citizens. In contrast\, in SA2\, a different approach was 
 taken\, custom-defined zones were created to analyze object counts and det
 ermine the percentage of people or vehicles using specific parts of the ar
 ea compared to the rest. \n\nIn this context\, the challenges encountered 
 during the project will be reported\, primarily those related to data tran
 smission. Due to the large amount of data collected\, it was difficult to 
 transmit everything using only an NB-IoT connection via the MQTT standard\
 , which\, due to its low bandwidth\, cannot handle the transmission of lar
 ge amounts of data. \n\nThanks to this research\, new advancements have be
 en made using this device firstly developed during the INSUBRIPARKS projec
 t\, such as analysis based on object tracking coordinates rather than sole
 ly relying on object counts. However\, further developments are needed\, i
 ncluding the possibility of georeferencing the data\, since the current sy
 stem uses an absolute reference system based on image coordinates\, and im
 proving the overall performance of the device. One of the critical aspects
  in this regard is the video streaming frame rate\, which currently ranges
  from 15 to 19 FPS. A more powerful device\, combined with a higher-resolu
 tion camera\, could achieve 30–40 FPS\, which would enhance both detecti
 on accuracy and the ability to track object positions more precisely durin
 g video capture. \n\nIn conclusion\, this paper presents and analyses the 
 collected data\, along with the preliminary results derived from the imple
 mented methodology\, where tracking data served as the raw input for all a
 nalyses. This approach is highly promising in providing valuable insights 
 for urban planners to improve the studied areas\, enhancing security\, and
  supporting sustainable and inclusive urban development.
DTSTAMP:20260527T053248Z
LOCATION:PA01 (Quarticle)
SUMMARY:OpenTrack: a Sensor for Monitoring the Usage of Territory - Daniele
  Strigaro
URL:https://talks.staging.osgeo.org/foss4g-europe-2025/talk/8NPSRZ/
END:VEVENT
END:VCALENDAR