This VCO introduces the concept of transfer learning from ImageNet to geospatial analysis. In our session, we explore how a model that has been pretrained on the vast ImageNet dataset can be effectively adapted to solve real-world geospatial problems, such as urban land cover classification. Participants will gain insight into the methodology of transfer learning, including the process of fine-tuning a powerful convolutional neural network to work with satellite and aerial imagery. We will compare a model trained from scratch with one that utilizes the pretrained features from ImageNet, highlighting the advantages in performance and efficiency.