Predicting urban tree cover from incomplete point labels and limited background information

• URL: http://arxiv.org/abs/2311.11592v1
• Date: Mon, 20 Nov 2023 08:09:54 GMT
• Title: Predicting urban tree cover from incomplete point labels and limited background information
• Authors: Hui Zhang, Ankit Kariryaa, Venkanna Babu Guthula, Christian Igel, Stefan Oehmcke
• Abstract summary: Trees inside cities are important for the urban microclimate, contributing positively to the physical and mental health of the urban dwellers. Despite their importance, often only limited information about city trees is available. We propose a method for mapping urban trees in high-resolution aerial imagery using limited datasets and deep learning.
• Score: 8.540501469749993
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Trees inside cities are important for the urban microclimate, contributing positively to the physical and mental health of the urban dwellers. Despite their importance, often only limited information about city trees is available. Therefore in this paper, we propose a method for mapping urban trees in high-resolution aerial imagery using limited datasets and deep learning. Deep learning has become best-practice for this task, however, existing approaches rely on large and accurately labelled training datasets, which can be difficult and expensive to obtain. However, often noisy and incomplete data may be available that can be combined and utilized to solve more difficult tasks than those datasets were intended for. This paper studies how to combine accurate point labels of urban trees along streets with crowd-sourced annotations from an open geographic database to delineate city trees in remote sensing images, a task which is challenging even for humans. To that end, we perform semantic segmentation of very high resolution aerial imagery using a fully convolutional neural network. The main challenge is that our segmentation maps are sparsely annotated and incomplete. Small areas around the point labels of the street trees coming from official and crowd-sourced data are marked as foreground class. Crowd-sourced annotations of streets, buildings, etc. define the background class. Since the tree data is incomplete, we introduce a masking to avoid class confusion. Our experiments in Hamburg, Germany, showed that the system is able to produce tree cover maps, not limited to trees along streets, without providing tree delineations. We evaluated the method on manually labelled trees and show that performance drastically deteriorates if the open geographic database is not used.

Related papers

• PureForest: A Large-Scale Aerial Lidar and Aerial Imagery Dataset for Tree Species Classification in Monospecific Forests [0.0]
  We present the PureForest dataset: a large-scale, open, multimodal dataset designed for tree species classification. Most current public Lidar datasets for tree species classification have low diversity as they only span a small area of a few dozen annotated hectares at most. In contrast, PureForest has 18 tree species grouped into 13 semantic classes, and spans 339 km$2$ across 449 distinct monospecific forests.
  arXiv  Detail & Related papers  (2024-04-18T10:23:10Z)
• Effective and Efficient Federated Tree Learning on Hybrid Data [80.31870543351918]
  We propose HybridTree, a novel federated learning approach that enables federated tree learning on hybrid data. We observe the existence of consistent split rules in trees and show that the knowledge of parties can be incorporated into the lower layers of a tree. Our experiments demonstrate that HybridTree can achieve comparable accuracy to the centralized setting with low computational and communication overhead.
  arXiv  Detail & Related papers  (2023-10-18T10:28:29Z)
• Task Planning Support for Arborists and Foresters: Comparing Deep Learning Approaches for Tree Inventory and Tree Vitality Assessment Based on UAV-Data [0.8889304968879161]
  Climate crisis and correlating prolonged, more intense periods of drought threaten tree health in cities and forests. We propose a novel open-source end-to-end approach that generates helpful information and improves task planning of those who care for trees in and around cities. Our approach is based on RGB and multispectral UAV data, which is used to create tree inventories of city parks and forests. Due to EU restrictions regarding flying drones in urban areas, we will also use multispectral satellite data and fifteen soil moisture sensors to extend our tree vitality-related basis of data.
  arXiv  Detail & Related papers  (2023-07-04T11:15:27Z)
• State of the Art and Potentialities of Graph-level Learning [54.68482109186052]
  Graph-level learning has been applied to many tasks including comparison, regression, classification, and more. Traditional approaches to learning a set of graphs rely on hand-crafted features, such as substructures. Deep learning has helped graph-level learning adapt to the growing scale of graphs by extracting features automatically and encoding graphs into low-dimensional representations.
  arXiv  Detail & Related papers  (2023-01-14T09:15:49Z)
• Monitoring Urban Forests from Auto-Generated Segmentation Maps [16.520025438843433]
  We present a weakly-supervised methodology to quantify the distribution of urban forests based on remotely sensed data with close-to-zero human interaction. As proof of concept we sense Hurricane Sandy's impact on urban forests in Coney Island, New York City (NYC) and reference it to less impacted urban space in Brooklyn, NYC.
  arXiv  Detail & Related papers  (2022-06-14T16:06:58Z)
• Automatic Quantification and Visualization of Street Trees [29.343663350855522]
  This work first explains a data collection setup carefully designed for counting roadside trees. We then describe a unique annotation procedure aimed at robustly detecting and quantifying trees. We propose a street tree detection, counting, and visualization framework using current object detectors and a novel yet simple counting algorithm.
  arXiv  Detail & Related papers  (2022-01-17T18:44:46Z)
• SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point Clouds [52.624157840253204]
  We introduce SensatUrban, an urban-scale UAV photogrammetry point cloud dataset consisting of nearly three billion points collected from three UK cities, covering 7.6 km2. Each point in the dataset has been labelled with fine-grained semantic annotations, resulting in a dataset that is three times the size of the previous existing largest photogrammetric point cloud dataset.
  arXiv  Detail & Related papers  (2022-01-12T14:48:11Z)
• Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges [52.624157840253204]
  We present an urban-scale photogrammetric point cloud dataset with nearly three billion richly annotated points. Our dataset consists of large areas from three UK cities, covering about 7.6 km2 of the city landscape. We evaluate the performance of state-of-the-art algorithms on our dataset and provide a comprehensive analysis of the results.
  arXiv  Detail & Related papers  (2020-09-07T14:47:07Z)
• Hidden Footprints: Learning Contextual Walkability from 3D Human Trails [70.01257397390361]
  Current datasets only tell you where people are, not where they could be. We first augment the set of valid, labeled walkable regions by propagating person observations between images, utilizing 3D information to create what we call hidden footprints. We devise a training strategy designed for such sparse labels, combining a class-balanced classification loss with a contextual adversarial loss.
  arXiv  Detail & Related papers  (2020-08-19T23:19:08Z)
• Weakly-Supervised Salient Object Detection via Scribble Annotations [54.40518383782725]
  We propose a weakly-supervised salient object detection model to learn saliency from scribble labels. We present a new metric, termed saliency structure measure, to measure the structure alignment of the predicted saliency maps. Our method not only outperforms existing weakly-supervised/unsupervised methods, but also is on par with several fully-supervised state-of-the-art models.
  arXiv  Detail & Related papers  (2020-03-17T12:59:50Z)
• Geocoding of trees from street addresses and street-level images [24.114405100879278]
  We introduce an approach for updating older tree inventories with geographic coordinates using street-level panorama images. Our method retrofits older inventories with geographic coordinates to allow connecting them with newer inventories to facilitate long-term studies on tree mortality etc.
  arXiv  Detail & Related papers  (2020-02-05T10:13:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.