The invention discloses a deep learning-based remote sensing image semantic segmentation method. The method comprises the steps of assigning an RGB value and a gray value to each target species, obtaining an original remote sensing image, selecting a target species to color and gray the target species, imparting a gray value to the target species to obtain a label image, and subjecting the original remote sensing image to data enhancement and edge extraction to obtain an edge-extracted image; training a full convolution neural network by adopting the original remote sensing image and the imagetraining sample of the edge-extracted image to obtain an optimum semantic segmentation network model, and inputting a to-be-tested remote sensing image into the optimum semantic segmentation networkmodel to obtain a semantic segmentation result image; coloring the semantic segmentation result image to obtain a final semantic segmentation result image, and obtaining a species target according toRGB values in the final semantic segmentation result image. According to the method, the semantic segmentation results of remote sensing images are high in accuracy, and the method is wide in applicability.