Plant.id excells in urban forest biodiversity study

In a new study focusing on urban forest biodiversity, Plant.id emerged as the leading application for the automatic identification of tree species, outperforming other methods in accuracy and efficiency. This research, conducted in Lleida, Spain, tested Plant.id alongside other apps, using Google Street View images to assess urban tree diversity. Plant.id's high success rate in identifying tree families, genera, and species showcases its academic credibility and practical utility. The findings not only highlight the app's technological prowess but also underscore its potential in urban planning and conservation efforts, reinforcing Kindwise's commitment to environmental stewardship.

Read the study:

Luisa Velasquez-Camacho, Esko Merontausta, Maddi Etxegarai, Sergio de-Miguel (2024) Assessing urban forest biodiversity through automatic taxonomic identification of street trees from citizen science applications and remote-sensing imagery, International Journal of Applied Earth Observation and Geoinformation, ISSN 1569-8432,
https://doi.org/10.1016/j.jag.2024.103735

At Kindwise, we can process not only Streetview data, but also other data from vehicles is equpipped with cameras and/or LiDar to enable us to help with automatic tree passport. If the resolution is fine enough, Plant.it can also identify shrubs and other plants, and also assess urban greenery health conditions.

The above study confirms the ability of Plant.id's ML models for this use case, as found in other studies, such as:

Irene Capecchi, Tommaso Borghini & Iacopo Bernetti (2023) Automated urban tree survey using remote sensing data, Google street view images, and plant species recognition apps, European Journal of Remote Sensing, 56:1, DOI: 10.1080/22797254.2022.2162441

We are glad to be able to help pioneer this new field of research.

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