Publication
Why should we confine land cover classes to rigid and arbitrary definitions? Land cover mapping is a central task in remote sensing image processing, but the rigorous class definitions can sometimes restrict the transferability of annotations between datasets. Open vocabulary recognition, i.e. using natural language to define a specific object or pattern in an image, breaks free from predefined nomenclature and offers flexible recognition of diverse categories with a more general image understanding across datasets and labels. The open vocabulary framework opens doors to search for concepts of interest, beyond individual class boundaries. In this work, we propose to use Text As supervision for COntrastive Semantic Segmentation (TACOSS), and we design an open vocabulary semantic segmentation model that extends its capacities beyond that of a traditional model for land cover mapping: In addition to visual pattern recognition, TACOSS leverages the common sense knowledge captured by language models and is capable of interpreting the image at the pixel level, attributing semantics to each pixel and removing the constraints of a fixed set of land cover labels. By learning to match visual representations with text embeddings, TACOSS can transition smoothly from one set of labels to another and enables the interaction with remote sensing images in natural language. Our approach combines a pretrained text encoder with a visual encoder and adopts supervised contrastive learning to align the visual and textual modalities. We explore several text encoders and label representation methods and compare their abilities to encode transferable land cover semantics. The model's capacity to predict a set of different land cover labels on an unseen dataset is also explored to illustrate the generalization capacities across domains of our approach. Overall, TACOSS is a general method and permits adapting between different sets of land cover labels with minimal computational overhead. Code is publicly available online1.