LEAFLET 2.0

The LEAFLET 2.0 (LightdronE Aerial support For pubLic grEen managemenT) project aims to develop a methodology and a service to support professional operators in the assessment of the health status and structural stability of public green areas, through the integration of data acquired by means of low-cost UAS (Unmanned Aerial System) platforms, WebGIS tools and through the use of Artificial Intelligence (AI) based techniques.

The continuous increase in the number of adverse climatic events, many of which are of an extreme nature, coupled with the constant ageing of tree installations in cities, leads the future of urban green management by technicians in the sector towards many challenges and associated risks. These risks derive from the possible dangers associated with the instability of trees in urban areas, due to unsuitable climatic conditions and improper maintenance of the same, which can lead to damage to property and above all to people, with consequent civil and criminal liability for those in charge of caring for them. In view of this, it is necessary to maximise the safety of urban green areas, ensuring that they are monitored and treated appropriately to avert any risk.

In order to do this, it is useful to clearly define standardised processes for the management of urban greenery, especially considering the aid of new technologies such as drones. This must include the description of procedures for planning the surveys, processing the acquired data, analysing the processed data and returning synthesised information through simplified tools to support professionals. In order to simplify the analysis and presentation of salient information to technicians, the LEAFLET2.0 project envisages the use of Artificial Intelligence (AI) and Machine Learning (ML) methodologies. These methodologies support the analysis of heterogeneous data (RGB, multispectral, LiDAR) and facilitate the rapid and automated generation of usable results for professionals.

Furthermore, in order to guarantee the creation of a complete and effective protocol, it is intended to directly involve local administrations in the study and definition processes, specifically the bodies in charge of monitoring, caring for and supervising urban green areas. Among these, for example, the green and environmental management office of the Municipality of Merano, which has shown its interest and availability towards the project, will certainly be involved, and the involvement of other offices dedicated to the management of public green areas of the South Tyrolean and national administrations will be evaluated.

Technical Objectives

  • Identification of the type of data needed to correctly represent the phenomenon and/or the state of health of the individual plant and/or the generic vegetative state of an entire urban green area;
  • Fine-tuning of the survey methodology, according to the specific in-depth study to be carried out;
  • Streamlining of data processing procedures applied to date;Processing of Big data, definition of an application methodology;Interface between the LEAFLET2.0 application and WebGIS GreenSpaces
  • Creation of thematic maps related to the analysis situation and definition of the significant outputs for the individual application cases and specific insights.

Innovation Objectives

  • Integration of heterogeneous data: in particular, the innovative character resides in the identification of an application integration protocol between the analysis of data from different sensor sources, such as images from RGB cameras, multispectral sensors and LiDAR, also succeeding in integrating them and applying the Visual Tree Assessment (VTA) methodology.
  • Integration of data from traditional surveys with high-resolution surveys carried out using UAS: the innovative character lies in the high potential for extending the possible studies that can be carried out, in addition to the representativeness that can be maintained by integrating traditional studies with the innovative ones; this aspect makes it possible to maintain a common thread between the monitoring data filed and recorded over the years in order to verify the correctness, consistency and continuity of the data and the innovative results obtained.
  • Use of innovative Artificial Intelligence methodologies for the analysis of data, especially heterogeneous data, and Computer Vision for the correct alignment of nadiral photographs, with the aim of being taken precisely on the crowns of the plants.
  • Simplification and automation of the process of compiling authorisation documents for flying in urban areas

Finally, part of the experimental project objectives will consider a validation of the survey results carried out also outside the province of Bozen/Bolzano. The activity will be punctual in order to explore meteorological and forest conditions that differ from the South Tyrolean context.

Project details

Title: LightdronE Aerial support For pubLic grEen managemenT
Acronym: LEAFLET2.0
Project Code: 244-22
CUP: B57H23000060001
Funding Programme: Autonomous Province of Bolzano (Provincial Law 13.12.2006, n. 14)
Overall Budget: € 220,300.00 (funded: € 220,300.00)
MAVTech Budget: € 90,135.00 (funded: € 90,135.00)
Consultants: Studio Verde, R3GIS, Flyingbasket.
Duration: 03.2023 – 12.2025