In part 1 of this blog post, we downloaded ~25.000 images of 100 plant species and trained a deep learning classification model. The 100 plant species are included in the Danish stream plant index (DVPI). In part 2, we create a REST API with endpoints/services that can be accessed from a very simple landing page.
All code from parts 1 and 2 of this blog post can be found on GitHub.
Plants species can be truly difficult to tell apart and this job often requires expert knowledge. However, when images are available computer vision methods can be used to guide us in the right direction. Deep learning methods are very useful for image analysis. Training convolutional neural networks have become the way to solve a wide range of image task including segmentation, classification, etc. Here, we will train a lightweight image classification model to identify 100 different plant species.
Estimating depths in lakes using machine learning
Recreational-grade sonar equipment can collect vast amounts of data. Unfortunately, the data is often hidden in some kind of proprietary binary format. However, efforts in reverse engineering such formats have made it possible to extract of the information. I have spent time tracking down some this information which has resulted in a R-package as well which can read ‘.sl2’ and ‘.sl3’ file formats collected using Lowrance sonar equipment. See also the sllib Python library which fills a similar gap.
Satellite imagery are collected at large scale and made freely available by institutions ESA and NASA. This data is collected at high spatial (10-30 m) and temporal (~2 weeks) resolution making it ideal for many applications. However, going from raw satellite imagery to nice looking image mosaics can be quite a mouthful. Here, I show how to use the gdalcubes R-package to produce a nationwide image mosaic of Denmark.
Digital elevation models (DEMs) are very convenient for modeling water flow. Some of the applications include delineation of watersheds, flowlines, or deriving useful other useful measures such as the ‘height above nearest drainage’ (HAND, link to another post on this). As a consequence of climate change, the frequency of extreme precipitation events is expected to increase in the future. Therefore, knowing the whereabouts of water is highly relevant and an important tool for the management of surface water in the landscape.
Developing the 'sonaR' R-package for reading and processing sonar data
Wind traveling across water surfaces creates waves. Wave action depends on several parameters including fetch which is the unobstructed length which the wind can travel across a water surface from a given direction. Areas with high wind fetch are often exposed areas but this also depends on the primary wind direction. By calculating wind fetch, we can quantify exposure of different areas and shorelines in waterbodies.