Update README.md

README.md

@@ -49,6 +49,9 @@ This document primarily lists resources for performing deep learning (DL) on sat
 ### Shuttle Radar Topography Mission (digital elevation maps)
 * [Data - open access](http://srtm.csi.cgiar.org/srtmdata/)
 
+### Aerial imagery (drones)
+* [Stanford Drone Dataset](http://cvgl.stanford.edu/projects/uav_data/)
+
 ## Kaggle
 Kaggle hosts several large satellite image datasets ([> 1 GB](https://www.kaggle.com/datasets?sortBy=relevance&group=public&search=image&page=1&pageSize=20&size=large&filetype=all&license=all)). A list if general image datasets is [here](https://gisgeography.com/free-satellite-imagery-data-list/). A list of land-use datasets is [here](https://gisgeography.com/free-global-land-cover-land-use-data/). The [kaggle blog](http://blog.kaggle.com) is an interesting read.
 
@@ -328,6 +331,9 @@ This section explores the different techniques (DL, ML & classical) people are a
 * [Removing speckle noise from Sentinel-1 SAR using a CNN](https://medium.com/upstream/denoising-sentinel-1-radar-images-5f764faffb3e)
 * A dataset which is specifically made for deep learning on SAR and optical imagery is the SEN1-2 dataset, which contains corresponding patch pairs of Sentinel 1 (VV) and 2 (RGB) data. It is the largest manually curated dataset of S1 and S2 products, with corresponding labels for land use/land cover mapping, SAR-optical fusion, segmentation and classification tasks. Paper: https://elib.dlr.de/128117/1/SEN12MS_Preprint.pdf Data: https://mediatum.ub.tum.de/1474000
 
+## Aerial imagery (drones)
+* [RetinaNet for pedestrian detection](https://towardsdatascience.com/pedestrian-detection-in-aerial-images-using-retinanet-9053e8a72c6)
+
 # For fun
 * [Style transfer - see the world in a new way](https://gist.github.com/jacquestardie/6227891818625e4c19c1b1d5bebe4fe4)