Introduction

Urban planning is undergoing a significant transformation, with a growing emphasis on incorporating green spaces and sustainable elements into urban environments. Tree detection in urban spaces has emerged as a crucial tool for urban planners and landscape architects to achieve this balance between urbanization and environmental preservation. In this blog, we will explore the importance of tree detection in urban spaces and shed light on the powerful YOLOv5 model, which has proven to be an exceptional tool in this domain.

The Significance of Tree Detection in Urban Spaces

1. Optimizing Urban Planning: Effective urban planning requires a comprehensive understanding of tree distribution and density within cities. By accurately detecting and mapping trees, urban planners can make informed decisions about the placement of new trees, parks, and green spaces. This not only enhances the aesthetic appeal of urban areas but also contributes to improved air quality, temperature regulation, and overall well-being of city dwellers.
2. Environmental Assessment: Tree detection in urban spaces enables environmental assessments that evaluate the health and quality of the urban ecosystem. By analyzing the distribution and characteristics of trees, environmentalists can monitor changes over time and develop strategies for conservation and environmental policies. Trees play a vital role in air purification, carbon sequestration, and mitigating the urban heat island effect, making their detection crucial for assessing the overall environmental impact of urban areas.
3. Data-driven Decision Making: Accurate tree detection relies on advanced models that can process large amounts of data efficiently. Among the various models available, YOLOv5 stands out as a reliable and efficient solution. It is a detection model that excels at identifying and localizing objects in images with remarkable precision and speed. This makes it a perfect fit for tree detection in urban spaces, where large-scale datasets need to be processed quickly.

The Power of YOLOv5 for Tree Detection

YOLOv5 architecture

The YOLOv5 model has gained significant attention in recent years due to its outstanding performance in object detection tasks. Its key features include:

• Accuracy and Speed: YOLOv5 strikes an excellent balance between accuracy and inference speed. It achieves high mAP (mean average precision) scores while providing real-time or near real-time predictions, making it ideal for our use case.
• Easy Deployment: YOLOv5 is built on the popular PyTorch framework, allowing for seamless integration into existing workflows. Its modular architecture facilitates easy customization and fine-tuning, enabling practitioners to adapt the model to specific requirements and datasets.

Dataset Description

The dataset we used for training contains 33 UAV (unmanned aerial vehicle) images. Each image has a width of 4400 and a height of 6600.

Experiment Results

We trained this data using our geolibs which is a wrapper library that integrates GeoEngine and MMLab libraries for GeoSpatial ML development.

Using our other utility library, granular-engine, we can track the experiments shown below.

Take a look at the following images to see the outcomes of our inference on randomly selected samples.

Sample Input Image

Output plotted on the Sample Image

Reference

• YOLOv5 by Ultralytics (Version 7.0) [Computer software]. Jocher, G. (2020). https://doi.org/10.5281/zenodo.3908559
• Europa: Increasing Accessibility of Geospatial Datasets. H Shin, N Exe, U Dutta, TR Joshi, S Verma, S Gupta. International Geoscience and Remote Sensing Symposium.
• GeoEngine: A Platform for Production-Ready Geospatial Research. S Verma, S Gupta, H Shin, A Panigrahi, S Goswami, S Pardeshi, N Exe, .. Computer Vision and Pattern Recognition Demo Track.