Cross-scale Urban Land Cover Mapping: Empowering Classification through Transfer Learning and Deep Learning Integration

Urban land cover mapping is essential for effective urban planning and resource management. Thanks to its ability to extract intricate features from urban datasets, deep learning has emerged as a powerful technique for urban classification. The U-net architecture has achieved state-of-the-art land cover classification performance, highlighting its potential for mapping urban trees at different spatial scales. However, deep learning approaches often require large, labeled datasets, which are challenging to acquire for specific urban contexts. Transfer learning addresses this limitation by leveraging pre-trained deep learning models on extensive datasets and adapting them to smaller urban datasets with limited labeled samples. Transfer learning can enhance classification performance and generalization ability. In this study, we proposed a novel cross-scale framework that integrates transfer learning and deep learning for urban land cover mapping. The framework utilizes pre-trained deep learning models, trained on diverse urban datasets, as a foundation for classification. These models are then finetuned using transfer learning techniques on smaller urban datasets, tailoring them to the specific characteristics of the target urban context. To evaluate the effectiveness and feasibility of the proposed framework, extensive evaluations are conducted across different cities and years. Performance metrics such as accuracy and dice score are employed to assess the framework\u27s classification capabilities. The results of this study contribute to advancing the field of urban classification by demonstrating the effectiveness and feasibility of the cross-scale framework. By combining transfer learning and deep learning, the framework improves classification accuracy, efficiency, and scalability in urban land cover mapping tasks. Leveraging the strengths of transfer learning and deep learning holds great promise for accurate and efficient urban land cover mapping, providing valuable insights for urban planning and resource management decision-making

Domain Agnostic Internal Distributions for Unsupervised Model Adaptation

We develop an algorithm for sequential adaptation of a classifier that is trained for a source domain to generalize in a unannotated target domain. We consider that the model has been trained on the source domain annotated data and then it needs to be adapted using the target domain unannotated data when the source domain data is not accessible. We align the distributions of the source and the target domains in a discriminative embedding space via an intermediate internal distribution. This distribution is estimated using the source data representations in the embedding space. We provide theoretical analysis and conduct extensive experiments on several benchmarks to demonstrate the proposed method is effective

Unsupervised Model Adaptation for Continual Semantic Segmentation

We develop an algorithm for adapting a semantic segmentation model that is trained using a labeled source domain to generalize well in an unlabeled target domain. A similar problem has been studied extensively in the unsupervised domain adaptation (UDA) literature, but existing UDA algorithms require access to both the source domain labeled data and the target domain unlabeled data for training a domain agnostic semantic segmentation model. Relaxing this constraint enables a user to adapt pretrained models to generalize in a target domain, without requiring access to source data. To this end, we learn a prototypical distribution for the source domain in an intermediate embedding space. This distribution encodes the abstract knowledge that is learned from the source domain. We then use this distribution for aligning the target domain distribution with the source domain distribution in the embedding space. We provide theoretical analysis and explain conditions under which our algorithm is effective. Experiments on benchmark adaptation task demonstrate our method achieves competitive performance even compared with joint UDA approaches.Comment: 12 pages, 5 figure