# Deep Learning {.unnumbered}

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# Introduction 

The Ship Detection tutorial explored a use case in which we might want to monitor the activity of ships in a particular location. That was a fairly straightforward task: the sea is very flat, and ships (especially large cargo and military vessels) protrude significantly. Using radar imagery, we could just set a threshold because if anything on the water is reflecting radio waves, it's probably a ship. 

One shortcoming of this approach is that it doesn't tell us what *kind* of ship we've detected. Sure, you could use the shape and size to distinguish between a fishing vessel and an aircraft carrier. But what about ships of similar sizes? Or what if you wanted to use satellite imagery to identify things other than ships, like airplanes, cars, or bridges? This sort of task-- called **"object detection"** is a bit more complicated. 

In this tutorial,

1. Object detection in satellite imagery  
2. Training a deep learning model on a custom dataset
3. Dynamic inference using Google Earth Engine


## Object Detection in Satellite Imagery 

Object detction in satellite imagery has a variety of useful applications. Immediately prior to the invasion of Ukraine, for example, a number of articles emerged 

    - monitoring a large area
    - monitoring a smaller area over time
    - lenvls




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![](images/obj_det2.jpg)

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## Training

YOLO is 

## Inference 

