The land use mapping refers to mapping and assessing changes and patterns of land use. The use of agricultural land maps becomes increasingly important. The governments, private sectors, research agencies, and community groups rely on land use mapping data for natural resource assessment, monitoring, and planning. Finding an effective mapping approach is thereby crucial for natural resource condition monitoring and investment, agricultural productivity, sustainability and planning, biodiversity conservation, natural disaster management, and bio-security. In this paper, four machine learning algorithms, i.e., the classic k-Nearest Neighbour (kNN), Support Vector Machines (SVMs), Convolutional Neural Network (CNN), and newly developed Capsule Network (CapsNet), are applied to classify satellite images for land use. For comprehensively comparing the performance of different algorithms for land use mapping, the experiments have been conducted on real-world datasets. Based on the experiment results, several improvements on the algorithms are proposed in order to fulfil the requirement of a large-scale land mapping. In addition, we design and implement these algorithms for land use mapping in a Machine Learning Land Use Mapping (ML-LUM) system. The system is able to train the models, predict classifications of satellite images, map the land use, display the land use statistic data, and predict production yields. With a friendly graphic user interface for farmers, the system is implemented by using the cloud computing technique for processing large land use data. Furthermore, we present a case study. For the case study, a banana plantation area from a given satellite image is correctly marked and the area size is then calculated, together with predicting banana production.