Tree Counter: Cyprus Tree Mapping with Deep Learning and Remote Sensing
The Motivation: As the world becomes increasingly urbanized, it is essential to monitor and manage urban green spaces. Urban forests, which consist of trees growing in urban areas, provide valuable ecosystem services such as reducing air pollution, mitigating the urban heat island effect, and providing recreational opportunities. However, mapping urban forests can be a challenging task, given the complex and dynamic nature of urban landscapes. In this blog post, we will explore a recent project that used remote sensing and deep learning techniques to map urban forests in the island of Cyprus.
The Method: The project involved training deep learning models on high spatial resolution satellite imagery to cover a total area of 9,000 square kilometers. Labeled satellite images were utilized by manually annotating the trees in remote sensing images. Subsequently, a dataset was generated and employed to train deep-learning models. These models could automatically detect and count trees in all the images covering the Island. The models were taught to identify trees based on their canopy, which is the uppermost layer of leaves and branches that cover the crown of the tree.
The Result: The success of the project was assessed using precision and recall metrics. The project achieved a precision and recall of 90%, indicating a high level of accuracy in detecting and mapping trees. The output of the project was a map of all trees on the Island of Cyprus, created as a GIS layer. The map revealed 54 million trees around the island, providing valuable information for environmental studies and land use planning.
The Importance: The data from the project can be used to study the distribution and diversity of trees on the island, as well as their ecological and economic value. The success of the project on the Island of Cyprus serves as an excellent example of how these techniques can be used to create valuable environmental data for large areas. By combining deep learning models with high spatial resolution satellite imagery, researchers can detect and map features on the Earth’s surface with remarkable accuracy. This data can then be used to inform decisions about land use, conservation, and natural resource management.
The Perspective: The success of this project highlights the potential for remote sensing and deep learning techniques to provide valuable insights into urban ecosystems. The use of these techniques can improve our understanding of urban forests, allowing us to better manage and protect these valuable resources. As cities continue to grow and expand, it is crucial to monitor and manage urban green spaces to ensure their sustainability and the well-being of urban residents. The mapping of urban forests using remote sensing and deep learning techniques represents a significant step forward in this regard, and we can expect to see further advancements in this area in the years to come.