Abstract: The present invention relates to a method for producing deep learning samples for geographic information extraction from remote sensing images. The samples can be produced by fusing two results from flood fill algorithm in image processing and deep learning model in artificial intelligence. In deep learning reasoning by changing an input image, such as rotation, translation, scaling, color & saturation adjustment and so on, multi-input images can be gotten and the corresponding output results are fused as a result by a rule of “Output by Bitwise Maximum Grayscale”. Finally the fusion result is perfected by man-machine interaction and supplemented to sample set. The method can improve the efficiency of producing deep learning samples, reduce the subjectivity of manual sample production and ensure the sample quality.