Implementations are described herein for automatically generating labeled synthetic images that are usable as training data for training machine learning models to make an agricultural prediction based on digital images. A method includes: generating a plurality of simulated images, each simulated image depicting one or more simulated instances of a plant; for each of the plurality of simulated images, labeling the simulated image with at least one ground truth label that identifies an attribute of the one or more simulated instances of the plant depicted in the simulated image, the attribute describing both a visible portion and an occluded portion of the one or more simulated instances of the plant depicted in the simulated image; and training a machine learning model to make an agricultural prediction using the labeled plurality of simulated images.

A method and an apparatus for training a classification model and data classification includes: obtaining a sample set and a pre-trained classification model, wherein the classification model includes at least two convolutional layers, each convolutional layer is connected to a classification layer through a fully connected layer; inputting the sample set into the classification model, and obtaining a prediction result output by each classification layer, wherein the prediction result includes a prediction probability of a class to which each sample belongs; calculating a probability threshold of each classification layer based on the prediction result output by each classification layer; setting a prediction stopping condition for the classification mode according to the probability threshold of each classification layer.

A method and an apparatus for training a classification model and data classification includes: obtaining a sample set and a pre-trained classification model, wherein the classification model includes at least two convolutional layers, each convolutional layer is connected to a classification layer through a fully connected layer; inputting the sample set into the classification model, and obtaining a prediction result output by each classification layer, wherein the prediction result includes a prediction probability of a class to which each sample belongs; calculating a probability threshold of each classification layer based on the prediction result output by each classification layer; setting a prediction stopping condition for the classification mode according to the probability threshold of each classification layer.

A landslide recognition method based on Laplacian pyramid remote sensing image fusion includes: performing original remote sensing image reconstruction based on extracted local features and global features of remote sensing images through a Laplacian pyramid fusion module to generate a fused image, constructing a deep learning semantic segmentation model through a semantic segmentation network, labeling the fused image to obtain a dataset of landslide disaster label map, and training the deep learning semantic segmentation model by the dataset, and then storing when a loss curve is fitted and a landslide recognition accuracy of remote sensing image of the deep learning semantics segmentation model meets a requirement by modifying a structure of the semantic segmentation network and adjusting parameters of the deep learning semantics segmentation model. Combined with the image fusion model based on Laplacian pyramid, the method can provide effective decision-making basis for prevention and mitigation of landslide disasters.

A landslide recognition method based on Laplacian pyramid remote sensing image fusion includes: performing original remote sensing image reconstruction based on extracted local features and global features of remote sensing images through a Laplacian pyramid fusion module to generate a fused image, constructing a deep learning semantic segmentation model through a semantic segmentation network, labeling the fused image to obtain a dataset of landslide disaster label map, and training the deep learning semantic segmentation model by the dataset, and then storing when a loss curve is fitted and a landslide recognition accuracy of remote sensing image of the deep learning semantics segmentation model meets a requirement by modifying a structure of the semantic segmentation network and adjusting parameters of the deep learning semantics segmentation model. Combined with the image fusion model based on Laplacian pyramid, the method can provide effective decision-making basis for prevention and mitigation of landslide disasters.

A floor plan image generating methodology is provided. The methodology includes: acquiring a boundary of a target building and a layout constraint of the target building; outputting multiple first floor plan images according to the layout constraint of the target building; selecting multiple second floor plan images from the multiple first floor plan images; applying a layout constraint of each of the second floor plan images to the boundary of the target building, and obtaining a layout of the target building corresponding to each of the multiple second floor plan images; inputting the layout of the target building and the boundary of the target building into a floor plan image generating network; and obtaining a predicted floor plan image of the target building outputted by the floor plan image generating network.

A floor plan image generating methodology is provided. The methodology includes: acquiring a boundary of a target building and a layout constraint of the target building; outputting multiple first floor plan images according to the layout constraint of the target building; selecting multiple second floor plan images from the multiple first floor plan images; applying a layout constraint of each of the second floor plan images to the boundary of the target building, and obtaining a layout of the target building corresponding to each of the multiple second floor plan images; inputting the layout of the target building and the boundary of the target building into a floor plan image generating network; and obtaining a predicted floor plan image of the target building outputted by the floor plan image generating network.

According to one embodiment, a learning apparatus includes a processor. The processor acquires first training data. The processor inputs the first training data to a model, and generate a plurality of estimation vectors that are a processing result of the model. The processor generates an estimation distribution from the estimation vectors. The processor calculates a distribution loss between the estimation distribution and a target distribution that is a target in an inference using the model. The processor updates parameters of the model, based on the distribution loss.

Information recommendation methods are provided. Image information corresponding to an image is obtained by processing circuitry. The image is associated with a user identifier. A user tag set corresponding to the user identifier and the image information is generated. A feature vector corresponding to user tags in the user tag set and the image information is formed. The feature vector is processed according to a trained information recommendation model, to obtain a recommendation parameter of to-be-recommended information. A recommendation of the to-be-recommended information is provided to a terminal corresponding to the user identifier according to the recommendation parameter.

Information recommendation methods are provided. Image information corresponding to an image is obtained by processing circuitry. The image is associated with a user identifier. A user tag set corresponding to the user identifier and the image information is generated. A feature vector corresponding to user tags in the user tag set and the image information is formed. The feature vector is processed according to a trained information recommendation model, to obtain a recommendation parameter of to-be-recommended information. A recommendation of the to-be-recommended information is provided to a terminal corresponding to the user identifier according to the recommendation parameter.