The field of urban environment monitoring and management is moving towards leveraging artificial intelligence and deep learning techniques to accurately assess and analyze urban green infrastructure. This includes estimating urban canopy coverage, monitoring urban development, and classifying trees outside forests. The use of aerial imagery, satellite imagery, and other remote sensing technologies is becoming increasingly popular for collecting data on urban environments. Noteworthy papers in this area include:

• A study that presents an efficient approach for estimating green canopy coverage using artificial intelligence and computer vision techniques, allowing for detailed analysis of urban vegetation and providing valuable insights for urban forestry management.
• A paper that introduces the Atlas Urban Index, a metric for measuring urban development computed using Sentinel-2 satellite imagery and Vision-Language Models, which overcomes the challenges of traditional urbanization indices and produces more reliable and stable development scores.
• A dataset for precision agriculture that provides a comprehensive collection of annotated top-view images of pastures, which can be used to advance the use of precision grazing management.
• A study that evaluates deep learning for mapping and classification of trees outside forests using high-resolution aerial imagery and achieves good classification accuracy across different landscapes.