RESUMO
The recent advancements in the field of deep learning have fundamentally altered the manner in which certain challenges and problems are addressed. One area that stands to greatly benefit from such innovations is the realm of urban planning, where the utilization of these tools can facilitate the automatic detection of landscape objects in a given area. However, it must be noted that these data-driven methodologies necessitate significant amounts of training data to attain desired results. This challenge can be mitigated through the application of transfer learning techniques, which reduce the amount of required data and permit the customization of these models through fine-tuning. The present study presents street-level imagery, which can be utilized for fine-tuning and deployment of custom object detectors in urban environments. The dataset comprises 763 images, each accompanied by bounding box annotations for five landscape object classes, including trees, waste bins, recycling bins, shop storefronts, and lighting poles. Furthermore, the dataset includes sequential frame data obtained from a camera mounted on a vehicle, capturing a total of three hours of driving, encompassing various regions within the city center of Thessaloniki.
