Using remotely sensed imagery to identify biophysical components across landscapes is an important avenue of investigation for ecologists studying ecosystem dynamics. With high-resolution remotely sensed imagery, algorithmic utilization of image context is crucial for accurate identification of biophysical components at large scales. In recent years, convolutional neural networks (CNNs) have become ubiquitous in image processing, and are rapidly becoming more common in ecology. Because the quantity of high-resolution remotely sensed imagery continues to rise, CNNs are increasingly essential tools for large-scale ecosystem analysis. We discuss here the conceptual advantages of CNNs, demonstrate how they can be used by ecologists through distinct examples of their application, and provide a walkthrough of how to use them for ecological applications. CNNs enable ecologists to identify biophysical components in high-resolution remotely sensed imagery by leveraging spatial context, and are particularly effective when ecological components have distinct shapes. CNNs can be used for both object detection, where key components are identified throughout an image, and semantic segmentation, where each pixel is classified individually. CNN accuracy is similar to human-level classification accuracy, but is consistent and fast, enabling rapid application over very large areas and/or through time.