Forests play a critical role in global carbon cycling, and continuous forest destruction together with other factors has accelerated global warming. Furthermore, continued decline of forest areas will critically hinder the accomplishment of carbon neutrality goals. Although the geographical location of deforestation can now be rapidly and accurately detected with remote sensing technology, current forest change products are still not fine-grained, especially from the perspective of carbon trading. Here, we used a deep learning method to detect deforestation in large regions based on 2 m high-resolution optical remote sensing images. Firstly, we proposed a new deforestation detection dataset, which was generated from 11 provincial regions in the Yangtze River Economic Zone of China, containing a total number of 8330 samples (the size of each sample being 512 × 512 pixels). Then, a new deforestation detection model, SiamHRnet-OCR, was designed, based on this dataset. Compared with other deep learning models, SiamHRnet-OCR achieves better results in terms of precision, F1-score, and OA indicator: 0.6482, 0.6892, and 0.9898, respectively. Finally, two large-scale scenarios of deforestation experiments in Southern China were further tested; the deforestation detection results demonstrate that SiamHRnet-OCR can not only detect deforestation effectively but also capture the accurate boundary of the changing area.