An information processing apparatus comprising: an acquisition means for acquiring a satellite image; a prediction means for cutting out the acquired satellite image into image patches to generate divided satellite images, for recursively predicting and generating a first image representing a distribution of population values with respect to the satellite image, through machine learning with one learning model, using the divided satellite images as input, and for predicting and generating a second image representing probabilities of land types with respect to the satellite image, through the machine learning with the one learning model, using the divided satellite images as input, the learning model including both a regression model that employs a regression approach for recursively predicting the first image and a classification model that employs a classification approach for predicting the second image; a training means for training the learning model for the machine learning, wherein the training means cuts out a plurality of satellite images into image patches to generate divided satellite images, cuts out a plurality of images representing land classes to generate divided images representing land classes, and cuts out a plurality of images representing a population to generate divided images representing a population, and the training means uses the divided satellite images cut out by the training means as input data, and the divided images representing land classes and the divided images representing a population that correspond to the divided satellite images cut out by the training means, as correct data, to train the learning model, wherein the learning model includes a first part to which the divided satellite images are input and a second part that outputs the first image and the second image that have been predicted, the first part includes a plurality of convolutional layers, and the second part includes a first branch and a second branch, and the first branch and the second branch each include a plurality of convolutional layers, wherein the first branch is formed as the regression model for predicting the first image and the second branch is formed as the classification model for predicting the second image. an acquisition means for acquiring a satellite image; a prediction means for cutting out the acquired satellite image into image patches to generate divided satellite images, for recursively predicting and generating a first image representing a distribution of population values with respect to the satellite image, through machine learning with one learning model, using the divided satellite images as input, and for predicting and generating a second image representing probabilities of land types with respect to the satellite image, through the machine learning with the one learning model, using the divided satellite images as input, the learning model including both a regression model that employs a regression approach for recursively predicting the first image and a classification model that employs a classification approach for predicting the second image; a training means for training the learning model for the machine learning, wherein the training means cuts out a plurality of satellite images into image patches to generate divided satellite images, cuts out a plurality of images representing land classes to generate divided images representing land classes, and cuts out a plurality of images representing a population to generate divided images representing a population, and the training means uses the divided satellite images cut out by the training means as input data, and the divided images representing land classes and the divided images representing a population that correspond to the divided satellite images cut out by the training means, as correct data, to train the learning model, wherein the learning model includes a first part to which the divided satellite images are input and a second part that outputs the first image and the second image that have been predicted, the first part includes a plurality of convolutional layers, and the second part includes a first branch and a second branch, and the first branch and the second branch each include a plurality of convolutional layers, wherein the first branch is formed as the regression model for predicting the first image and the second branch is formed as the classification model for predicting the second image.