In 2006, a complete inventory of all the City’s street trees, including trees located within sidewalks, between sidewalks and curbs, or within 6 feet of the street if no sidewalk existed was conducted. One hundred volunteers were trained to record address, location, tree species, tree diameter, condition, and other related information. Trees located in parks and other public property were not included. Approximately 25,000 street trees were counted and the data was loaded into a tree database that the Forestry Division uses daily to manage the trees, track tree work, and record constituent concerns.

Street tree data from the TreesCount! 2015 Street Tree Census, conducted by volunteers and staff organized by NYC Parks & Recreation and partner organizations. Tree data collected includes tree species, diameter and perception of health. Accompanying blockface data is available indicating status of data collection and data release citywide.

The 2015 tree census was the third decadal street tree census and largest citizen science initiative in NYC Parks’ history. Data collection ran from May 2015 to October 2016 and the results of the census show that there are 666,134 trees planted along NYC's streets. The data collected as part of the census represents a snapshot in time of trees under NYC Parks' jurisdiction.

The census data formed the basis of our operational database, the Forestry Management System (ForMS) which is used daily by our foresters and other staff for inventory and asset management: https://data.cityofnewyork.us/browse?sortBy=most_accessed&utf8=%E2%9C%93&Data-Collection_Data-Collection=Forestry+Management+System+%28ForMS%29

To learn more about the data collected and managed in ForMS, please refer to this user guide: https://docs.google.com/document/d/1PVPWFi-WExkG3rvnagQDoBbqfsGzxCKNmR6n678nUeU/edit. For information on the city's current tree population, use the ForMS datasets.

This dataset contains the extent of groups of trees covered by a tree preservation order. It can be used for managing and protecting important trees by preventing their unauthorised removal or damage. Members of the public and developers use the data to check if a tree is protected and to inform planning applications or tree work proposals. The data helps ensure trees remain a significant part of the local environment and public amenity. This dataset contains data from a small group of local planning authorities who we are working with to develop a data specification for tree preservation orders.

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This rich and expansive dataset offers a deep dive into the urban forest, featuring over 70,000 meticulously documented trees. Each entry captures the essence of the city's greenery through precise geospatial coordinates (latitude and longitude), allowing for accurate mapping and analysis of urban biodiversity.

Discover a diverse collection of trees with detailed botanical classifications, including genus and species names in both Latin and German, providing insights into the ecological variety thriving within the city. The dataset paints a vivid picture of each tree's stature with height measurements, trunk diameter, circumference, and crown spread, showcasing the structural complexity of urban vegetation.

Beyond physical attributes, the dataset reveals the urban fabric's social dimensions by indicating tree sponsorships and the presence of avenue trees that line the city’s streets. This makes it a powerful tool for analyzing community engagement with urban nature, planning sustainable green spaces, and understanding how trees contribute to urban ecosystems.

Whether you're an urban planner, environmental researcher, data scientist, or nature enthusiast, this dataset provides a comprehensive foundation for exploring how trees shape the health, beauty, and livability of urban environments.

Last updated 3/9/2016. In 2006, a complete inventory of all the City’s street trees, including trees located within sidewalks, between sidewalks and curbs, or within 6 feet of the street if no sidewalk existed was conducted. One hundred volunteers were trained to record address, location, tree species, tree diameter, condition, and other related information. Trees located in parks and other public property were not included. Approximately 25,000 street trees were counted and the data was loaded into a tree database that the Forestry Division uses daily to manage the trees, track tree work, and record constituent concerns.

The street tree cadastre includes the comprehensive representation of the trees on public street corridors as the basis of the tree checks for traffic safety and planning. Key data fields are: Location, genus/species, year of planting, crown circumference, stem diameter, biological parameters and damage characteristics. The fast graphical visualization of tree locations and background maps provides an integrated GIS component. The data is stored in a SQL Server database. It is recorded as part of ongoing surveys and updates. The updates also take place within the framework of the prescribed tree inspections for road safety.

The - online street tree register - includes the comprehensive representation of the trees on public street corridors with essential trunk attributes as well as the data of the Hamburg Port Authority, which are stored in a separate database. The following data fields are assigned to each tree: Location, genus/species, year of planting, crown diameter and trunk circumference. This data, which is collected as part of regular tree checks, is updated annually for the Internet as of 1 January. They are available for download as WMS and WFS services as well as in GML format.

Overview

This dataset on trees covers Dublin City, Ireland. It indicates the locations of over 300,000 trees which were acquired using a point-and-click methodology using a high-quality aerial image (resolution of 12.5 cm) taken in June 2018. Each x,y location represents the tree canopy centre. The database also includes the estimated height of these trees based on a Digital Elevation Model (horizontal resolution of 1m, vertical resolution *m). This information is complemented with detailed information on individual trees, where available. This includes, for example, data on 2440 street trees (species and dimensions) gathered in 2008/2009. The dataset will be updated at regular intervals as information on individual trees is acquired.

• to be updated

Purpose

Information on trees (location, species, age, and health) in urban areas is needed to assess the green infrastructure and the ecosystem, environmental and social services that they provide. These data were acquired to support greening strategies and actions in the Dublin City Council area.

Additional comments

The creation of the Dublin tree database was supported in part by a research grant from Ireland’s Environmental Protection Agency (EPA) and using databases acquired by the School of Geography at UCD. The tree database is part of the Mapping Green Dublin project and is being used by the Curio app, which allows citizens to add data on individual trees.

The Fire and Tree Mortality (FTM) database includes standardized observations of fire injury and survival or mortality for 171,177 individual tree-level observations, representing 142 tree species across the United States. Of these, 7,191 trees have burned twice. These trees were burned in 420 prescribed fires and wildfires occurring in 35 years, from 1981 to 2016. The database was developed using 41 contributed datasets from researchers, managers, and archived data products. At a minimum, datasets had to contain measurements of individual trees, size, fire injury, and post-fire survival, but some datasets include additional data such as bark beetle attack. Only trees that were alive before the fire were included in the database. We included any trees where post-fire status was measured within 10 years of the fire. If a tree re-burned in a subsequent fire, and post-fire injury and status information were available after that fire, then a new record (row) was made for that tree aft ...

Tree Species information from the Parks Department. Data are updated by city staff as needed, and copied to VicMap and the Open Data Portal on a weekly basis. Parks Department tree species data are collected by GPS location. For surveyed trees maintained by the Engineering Department, please see the Surveyed Trees layer.Diameter at Breast Height (DBH) is in centimetres. Tree Canopy Height and Width are in metres.The "Last Updated" date shown on our Open Data Portal refers to the last time the data schema was modified in the portal, or any changes were made to this description. We update our data through weekly scripts which does not trigger the "last updated" date to change.Note: Attributes represent each field in a dataset, and some fields will contain information such as ID numbers. As a result some visualizations on the tabs on our Open Data page will not be relevant.

Citywide street tree data from the 2005 Street Tree Census, conducted partly by volunteers organized by NYC Parks & Recreation. Trees were inventoried by address, and were collected from 2005-2006. Data collected includes tree species, diameter, condition.

Sustainable cities depend on urban forests. City trees -- a pillar of urban forests -- improve our health, clean the air, store CO2, and cool local temperatures. Comparatively less is known about urban forests as ecosystems, particularly their spatial composition, nativity statuses, biodiversity, and tree health. Here, we assembled and standardized a new dataset of N=5,660,237 trees from 63 of the largest US cities. The data comes from tree inventories conducted at the level of cities and/or neighborhoods. Each data sheet includes detailed information on tree location, species, nativity status (whether a tree species is naturally occurring or introduced), health, size, whether it is in a park or urban area, and more (comprising 28 standardized columns per datasheet). This dataset could be analyzed in combination with citizen-science datasets on bird, insect, or plant biodiversity; social and demographic data; or data on the physical environment. Urban forests offer a rare opportunity to intentionally design biodiverse, heterogenous, rich ecosystems. Methods See eLife manuscript for full details. Below, we provide a summary of how the dataset was collected and processed.

Data Acquisition We limited our search to the 150 largest cities in the USA (by census population). To acquire raw data on street tree communities, we used a search protocol on both Google and Google Datasets Search (https://datasetsearch.research.google.com/). We first searched the city name plus each of the following: street trees, city trees, tree inventory, urban forest, and urban canopy (all combinations totaled 20 searches per city, 10 each in Google and Google Datasets Search). We then read the first page of google results and the top 20 results from Google Datasets Search. If the same named city in the wrong state appeared in the results, we redid the 20 searches adding the state name. If no data were found, we contacted a relevant state official via email or phone with an inquiry about their street tree inventory. Datasheets were received and transformed to .csv format (if they were not already in that format). We received data on street trees from 64 cities. One city, El Paso, had data only in summary format and was therefore excluded from analyses.

Data Cleaning All code used is in the zipped folder Data S5 in the eLife publication. Before cleaning the data, we ensured that all reported trees for each city were located within the greater metropolitan area of the city (for certain inventories, many suburbs were reported - some within the greater metropolitan area, others not). First, we renamed all columns in the received .csv sheets, referring to the metadata and according to our standardized definitions (Table S4). To harmonize tree health and condition data across different cities, we inspected metadata from the tree inventories and converted all numeric scores to a descriptive scale including “excellent,” “good”, “fair”, “poor”, “dead”, and “dead/dying”. Some cities included only three points on this scale (e.g., “good”, “poor”, “dead/dying”) while others included five (e.g., “excellent,” “good”, “fair”, “poor”, “dead”). Second, we used pandas in Python (W. McKinney & Others, 2011) to correct typos, non-ASCII characters, variable spellings, date format, units used (we converted all units to metric), address issues, and common name format. In some cases, units were not specified for tree diameter at breast height (DBH) and tree height; we determined the units based on typical sizes for trees of a particular species. Wherever diameter was reported, we assumed it was DBH. We standardized health and condition data across cities, preserving the highest granularity available for each city. For our analysis, we converted this variable to a binary (see section Condition and Health). We created a column called “location_type” to label whether a given tree was growing in the built environment or in green space. All of the changes we made, and decision points, are preserved in Data S9. Third, we checked the scientific names reported using gnr_resolve in the R library taxize (Chamberlain & Szöcs, 2013), with the option Best_match_only set to TRUE (Data S9). Through an iterative process, we manually checked the results and corrected typos in the scientific names until all names were either a perfect match (n=1771 species) or partial match with threshold greater than 0.75 (n=453 species). BGS manually reviewed all partial matches to ensure that they were the correct species name, and then we programmatically corrected these partial matches (for example, Magnolia grandifolia-- which is not a species name of a known tree-- was corrected to Magnolia grandiflora, and Pheonix canariensus was corrected to its proper spelling of Phoenix canariensis). Because many of these tree inventories were crowd-sourced or generated in part through citizen science, such typos and misspellings are to be expected. Some tree inventories reported species by common names only. Therefore, our fourth step in data cleaning was to convert common names to scientific names. We generated a lookup table by summarizing all pairings of common and scientific names in the inventories for which both were reported. We manually reviewed the common to scientific name pairings, confirming that all were correct. Then we programmatically assigned scientific names to all common names (Data S9). Fifth, we assigned native status to each tree through reference to the Biota of North America Project (Kartesz, 2018), which has collected data on all native and non-native species occurrences throughout the US states. Specifically, we determined whether each tree species in a given city was native to that state, not native to that state, or that we did not have enough information to determine nativity (for cases where only the genus was known). Sixth, some cities reported only the street address but not latitude and longitude. For these cities, we used the OpenCageGeocoder (https://opencagedata.com/) to convert addresses to latitude and longitude coordinates (Data S9). OpenCageGeocoder leverages open data and is used by many academic institutions (see https://opencagedata.com/solutions/academia). Seventh, we trimmed each city dataset to include only the standardized columns we identified in Table S4. After each stage of data cleaning, we performed manual spot checking to identify any issues.

The database deals with the trees of India. The ToI, Ver.-I (Trees of India, Version-I) includes data on 3708 tree species distributed across 35 states/union territories of India. The database is based on systematic review of 313 literature sources published from 1872-2022. The database follows the scientific nomenclature as per Plants of the World Online (2022). The database also includes 609 species endemic to India, and 347 species currently threatened as per IUCN (2022).

This data publication contains urban tree inventory data for 929,823 street trees that were collected from 2006 to 2013 in 49 California cities. Fifty six urban tree inventories were obtained from various sources for California cities across five climate zones. The five climate zones were based largely on aggregation of Sunset National Garden Book's 45 climate zones. Forty-nine of the inventories fit the required criteria of (1) included all publicly managed trees, (2) contained data for each tree on species and diameter at breast height (dbh) and (3) was conducted after 2005. Tree data were prepared for entry into i-Tree Streets by deleting unnecessary data, matching species to those in the i-Tree database, and establishing dbh size classes. Data included in this publication include tree location (city, street name and number), diameter at breast height, species name and/or species code, and tree type.These data were used to calculate street tree stocking levels, species abundance, size diversity, function and value, which can be used to determine trends in tree number and density, identify priority investments and create baseline data against which the efficacy of future practices can be evaluated.

Trees cared for and managed by the City of Pittsburgh Department of Public Works Forestry Division.

Tree Benefits are calculated using the National Tree Benefit Calculator Web Service.

NOTE: The data in this dataset has not updated since 2020 because of a broken data feed. We're working to fix it.

This dataset shows point locations of public trees inventoried by the City of Perth. Data is compiled from field capture from our parks team. This is not a complete comprehensive inventory of all trees as trees in the private realm are excluded. Some errors and/or duplicate data may exist. Show full description

TreeAI - Advancing Tree Species Identification from Aerial Images with Deep Learning

• Register: Access to the data will be granted upon registering for the competition, see the registration form: https://form.ethz.ch/research/tree-ai-global-database/treeai-competition.html
• Request the dataset: Download the competition record after registration by requesting it. Enter your full name, purpose e.g. accept the TreeAI4Species data license, affiliation, and the country of affiliation in the request. This allows us to check whether you are already registered.
• Test dataset: Only the participants registered for the competition will receive the test dataset.
• Submit your DL models for evaluation by June 2025.
• Award: The best models win a prize.
• Publication: All participants in the competition who submit the required files for evaluation will be included in the subsequent publication.

License

This dataset provides updates on Tree data in Dublin region; encompassing columns such as ID, Age, Condition, Proximity, Building-Number, Street Area, Stem-Diameter, Spread Height and Species

Dataset

This dataset was created by Muhammad Usman

Released under CC0: Public Domain

Contents

This data publication contains urban tree growth data collected over a period of 14 years (1998-2012) in 17 cities from 13 states across the United States: Arizona, California, Colorado, Florida, Hawaii, Idaho, Indiana, Minnesota, New Mexico, New York, North Carolina, Oregon, and South Carolina.

Measurements were taken on over 14,000 urban street and park trees. Key information collected for each tree species includes bole and crown size, location, and age. Based on these measurements, 365 sets of allometric equations were developed for tree species from around the U.S. Each “set” consists of eight equations for each of the approximately 20 most abundant species in each of 16 climate regions. Tree age is used to predict a species diameter at breast height (dbh), and dbh is used to predict tree height, crown diameter, crown height, and leaf area. Dbh is also used to predict age. For applications with remote sensing, average crown diameter is used to predict dbh. There are 171 distinct species represented within this database. Some species grow in more than one region. The Urban Tree Database (UTD) contains foliar biomass data (raw data and summarized results from the foliar sampling for each species and region) that are fundamental to calculating leaf area, as well as tree biomass equations (compiled from literature) for carbon storage estimates. An expanded list of dry weight biomass density factors for common urban species is made available to assist users in using volumetric equations.Information on urban tree growth underpins models used to calculate effects of trees on the environment and human well-being. Maximum tree size and other growth data are used by urban forest managers, landscape architects and planners to select trees most suitable to the amount of growing space, thereby reducing costly future conflicts between trees and infrastructure. Growth data are used to develop correlations between growth and influencing factors such as site conditions and stewardship practices. Despite the importance of tree growth data to the science and practice of urban forestry, our knowledge is scant. Over a period of 14 years scientists with the U.S. Forest Service recorded data from a consistent set of measurements on over 14,000 trees in 17 U.S. cities.These data were originally published on 03/02/2016. The metadata was updated on 10/06/2016 to include reference to a new publication. Minor metadata updates were made on 12/15/2016. On 01/07/2020 this data publication was updated to correct a few species' names and systematic errors in the data that were found. A complete list of these changes is included (\Supplements\Errata_Jan2020_RDS-2016-0005.pdf). In addition, we have included a list of changes for the General Technical Report associated with these data (\Supplements\Errata_Jan2020_PNW-GTR-253.pdf).

The public tree dataset includes a listing of public trees on boulevards and public trees in parks, in the City of Vancouver and provides data on tree coordinates, species and other related characteristics. Private trees are not included in the inventory.Tree records that do not have coordinates data will not show up in the list. Data currencyThe dataset refreshes daily on weekdays. Tree attributes are updated on a regular basis but it may be several years between updates for some attributes. Priorities and resources determine how fast a change in reality is reflected in the data. The coordinates were originally provided by the 2016 Geospatial Data for City of Vancouver Street Trees project. Data accuracyTree attributes are updated on a regular basis but it may be several years for some attributes. Note: 0 value in latitude and longitude fields mean there is no related information available Websites for further informationStreet Tree BylawCity trees