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Deep Learn Imagery

本课程是 Satellite Imagery, Remote Sensing & Machine Learning 专项课程的一部分

位教师：Professionals from the Industry

包含在中

了解更多

3个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

2 小时完成

灵活的计划

自行安排学习进度

3个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

2 小时完成

灵活的计划

自行安排学习进度

您将获得的技能

您将学习的工具

要了解的详细信息

可分享的证书

添加到您的领英档案

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累特定领域的专业知识

本课程是 Satellite Imagery, Remote Sensing & Machine Learning 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

向行业专家学习新概念
获得对主题或工具的基础理解
通过实践项目培养工作相关技能
获得可共享的职业证书

该课程共有3个模块

This hands-on course proves that deep learning isn't just about pressing "run" on a model. It's about turning satellite imagery into actual, useful insights. You'll work with convolutional neural networks for land cover classification, fine-tune a pre-trained CNN using transfer learning, use data augmentation to improve performance, and apply Grad-CAM to see where the model is actually looking. Along the way, you'll practice translating raw satellite imagery into insights you can clearly communicate to others. You are required to have basic Python programming, familiarity with machine learning concepts, and introductory knowledge of neural networks and image data. Designed for beginners in machine learning and remote sensing, Deep Learn Imagery builds your confidence in both working with deep learning and explaining what your models are doing.

单元详情

In this module, you will apply transfer learning techniques to fine-tune a pre-trained convolutional neural network (CNN) for land cover classification using satellite imagery. The module focuses on adapting existing vision models to geospatial data under real-world constraints such as limited labeled samples, class imbalance, and spatial generalization challenges.

涵盖的内容

2个视频2篇阅读材料2个作业

2个视频总计10分钟

Introduction and Welcome4分钟
Fine-Tuning Pre-Trained CNNs and Feature Reuse for Satellite Imagery 7分钟

2篇阅读材料总计11分钟

Avoiding Spatial Data Leakage in Land-Cover Classification Models 8分钟
Walkthrough - Fine-Tune a CNN for Land Cover Classification 3分钟

2个作业总计25分钟

Hands-On Learning: Fine-Tune a CNN for Land Cover Classification15分钟
Practice Quiz: CNN Fine-Tuning Decisions10分钟

In this module, learners design and apply data augmentation pipelines to improve the generalization of convolutional neural networks trained on satellite imagery. The module focuses on selecting realistic augmentations that preserve spatial meaning while addressing limited and imbalanced land-cover data.

涵盖的内容

2个视频2篇阅读材料1个作业

2个视频总计8分钟

Data Augmentation for Land-Cover Classification5分钟
Building an Augmentation Pipeline for CNN Training4分钟

2篇阅读材料总计10分钟

When Data Augmentation Hurts Model Performance8分钟
Walkthrough - Implement and Evaluate a Data Augmentation Pipeline2分钟

1个作业总计15分钟

Hands-On Learning: Implement and Evaluate a Data Augmentation Pipeline15分钟

In this module, learners use Grad-CAM visualizations to interpret convolutional neural network predictions for satellite imagery. The module emphasizes understanding model attention, identifying failure modes, and communicating model behavior clearly to technical and non-technical stakeholders.