Izrada kataloga uličnih drvoreda iz Google Mapsa

Prikaz članka u kojem su autori predložili automatizirani sustav izrade suvremene inventure stabala temeljen na javno dostupnim aerosnimkama visoke rezolucije i panoramama na uličnoj razini. Sustav prvo iz Google Mapsa preuzima sve dostupne aerosnimke i ulične panorame (Street View) određenog područja. Detektor stabala razlikuje stabla od svih drugih objekata na snimkama. Razvrstavač stabala u vrste prethodno je obučen na područjima gdje postoje takvi podaci

Geomatika u eri velikih podataka

Prikaz članka u kojem autori ističu da je geomatika uslužno usmjerena znanost. Krajnji cilj geoprostornih informacijskih usluga je pružanje pravih podataka, informacija ili znanja pravoj osobi na pravom mjestu u pravo vrijeme. Era velikih podataka daje geomatici povijesnu priliku da ostvari taj cilj

Opportunities for machine learning and artificial intelligence in national mapping agencies:enhancing ordnance survey workflow

National Mapping agencies (NMA) are frequently tasked with providing highly accurate geospatial data for a range of customers. Traditionally, this challenge has been met by combining the collection of remote sensing data with extensive field work, and the manual interpretation and processing of the combined data. Consequently, this task is a significant logistical undertaking which benefits the production of high quality output, but which is extremely expensive to deliver. Therefore, novel approaches that can automate feature extraction and classification from remotely sensed data, are of great potential interest to NMAs across the entire sector. Using research undertaken at Great Britain’s NMA; Ordnance Survey (OS) as an example, this paper provides an overview of the recent advances at an NMA in the use of artificial intelligence (AI), including machine learning (ML) and deep learning (DL) based applications. Examples of these approaches are in automating the process of feature extraction and classification from remotely sensed aerial imagery. In addition, recent OS research in applying deep (convolutional) neural network architectures to image classification are also described. This overview is intended to be useful to other NMAs who may be considering the adoption of similar approaches within their workflows

Automatic Large Scale Detection of Red Palm Weevil Infestation using Aerial and Street View Images

The spread of the Red Palm Weevil has dramatically affected date growers, homeowners and governments, forcing them to deal with a constant threat to their palm trees. Early detection of palm tree infestation has been proven to be critical in order to allow treatment that may save trees from irreversible damage, and is most commonly performed by local physical access for individual tree monitoring. Here, we present a novel method for surveillance of Red Palm Weevil infested palm trees utilizing state-of-the-art deep learning algorithms, with aerial and street-level imagery data. To detect infested palm trees we analyzed over 100,000 aerial and street-images, mapping the location of palm trees in urban areas. Using this procedure, we discovered and verified infested palm trees at various locations

Tools For Growth: A Case Study Processing UC Green\u27s Planting Records Using Remote Software Tools

Urban tree inventories typically require extensive field work for data collection, but a new software tool has been developed to remotely determine an urban forest’s features using publicly available online images. In this study, tree planting records from UC Green were processed for current features and environmental impacts using only remote data collection and data management tools. Trees in the organization’s planting record were first located geographically, identified by genus and species, and then algorithmically measured for diameter. After aggregating and verifying fifteen years of bi-annual planting records and processing them with the remote tools, the full record was entered into a live database to facilitate monitoring and maintenance, and then analyzed for its provision of ecosystem services. Out of 1485 street trees confirmed planted by the nonprofit, 1232 were found to be presently living with the most common species being Syringa reticulata (Japanese tree lilac), Acer rubrum (red maple), and Gleditsia triacanthos (Honey locust). Some key impacts of this work were determining the size and scope of the nonprofit’s planting accomplishments, as well as estimated ecosystem services, and the facilitation of future monitoring and planting operational performance assessment. The impacts of the UC Green’s tree plantings can be increased further as operations are augmented according to the suggested recommendations, which were based on the study’s results