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.

土地利用制图是指对土地利用的变化和格局进行制图和评估。农业土地图的使用变得越来越重要。政府，私营部门，研究机构和社区团体都依赖土地使用图数据进行自然资源评估，监测和规划。因此，找到有效的测绘方法对于自然资源状况监测和投资，农业生产力，可持续性和规划，生物多样性保护，自然灾害管理以及生物安全至关重要。

本文采用了四种机器学习算法，即经典k最近邻（kNN），支持向量机（SVM），卷积神经网络（CNN）和最新开发的胶囊网络（CapsNet），对卫星图像进行分类。供土地使用。为了全面比较不同土地利用制图算法的性能，已对真实数据集进行了实验。根据实验结果，对算法进行了若干改进，以满足大规模土地制图的要求。此外，我们在机器学习土地使用制图（ML-LUM）系统中设计和实现这些用于土地使用制图的算法。该系统能够训练模型，预测卫星图像的分类，绘制土地利用图，显示土地利用统计数据，并预测产量。该系统具有用于农民的友好图形用户界面，通过使用云计算技术来处理大量土地使用数据来实现该系统。此外，我们提出一个案例研究。对于案例研究，正确标记了给定卫星图像中的香蕉种植面积，然后计算出面积大小，并预测了香蕉的产量。