Determining the topographic map scale is a critical basis for assessing the degree of confidentiality of topographic maps.In this study,we propose a solution to the challenge of estimating the scale of fragmented raster topographic maps by leveraging a priori knowledge of scale-related features,constructing an expert knowledge image pyramid dataset(EKIPD)un-der guided expert knowledge,and applying deep convolutional neural network algorithms to create a hybrid intelligent model that synergistically combines knowledge,data,and algorithm.The EKIPD dataset captures a representative sample distribu-tion of fragmented topographic maps of varying sizes,which enables us to statistically determine the optimal recognition size(ORS)for sub-map recognition.The ORS then serves as a stepping threshold to partition the topographic maps into recogniza-ble sub-maps.Each sub-map is independently processed through the model to obtain individual predictions,which are subse-quently integrated to infer the map scale.Experimental validation shows that this method achieves an accuracy of approximate-ly 97％,demonstrating its efficacy.
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