uavgeo 0.2.2

UAV image processing library

uavgeo ⛰️

A UAV-specific Python image processing library built upon xarray and geopandas.

Explore the wiki »

Report Bug . Request Feature

Table Of Contents

• Summary
• Features
  • Index calculation
  • Dataset chipping
  • DTM creation
  • Dataset shadows
• Installation

Summary

UAV image analysis is a powerful tool to gain valuable insights into the rural, urban and natural environment. Especially in conjunction with Deep Learning, large strides can be made. The problem however is that there is little standardization and a lot of boilerplate code to be written for image analysis. This package serves to bridge the gap in image processing and machine learning in UAV applications. It builds upon the efforts from xarray, rasterio/rioxarray and geopandas. Importing should be done through rioxarray functions. The currently implemented functions in uavgeo examples cover index calculation (see below) and data/rastser chipping and reconstruction.

Features

The uavgeo package can be installed through pip. Additionally, a docker container with jupyterlab can be used. See the Installation section for more information.

Dataset chipping

Chipping is a prerequisite for geographic raster data to be processed for ML/DL models. This library implements it as follows:

1. creating a chips-geodataframe based on wanted dimensions, overlap and raster shape
2. chipping the input raster into a list of chips
3. reset the coordinates from crs to image pixels (numpy assumed dimensions)
4. export the list of images to file (or do whatever)
5. (optional): perform the ML modelling on the chips
6. (optional): reconstruct the images back to the original raster and crs

This whole pipeline and functions are presented in an example notebook

Creating a DTM with the calc_dtm_from_dsm function

The calc_dtm_from_dsm function is a utility to create a Digital Terrain Model (DTM) from a Digital Surface Model (DSM) using specified sampling parameters. It operates on data represented as xarray DataArray and relies on the rasterio library for Geographic Information System (GIS) operations. The resulting DTM is created by sampling and extracting the minimum elevation values from the DSM at a user-defined grid, built upon the chipping presented above.

An example can be found in an example notebook

Inputs:

• dsm (xr.DataArray): The input Digital Surface Model as an xarray DataArray.
• pixel_size (float): The pixel size in the same unit as the DSM data.
• sampling_meters (float): The distance in meters that defines the sampling grid for DEM creation.

import uavgeo as ug
import rioxarrary as rxr

dsm_data = rxr.open_rasterio(('dsm.tif')  # Load DSM data from a GeoTIFF file
pixel_size = 1.0  # Specify the pixel size in meters
sampling_distance = 10.0  # Define the sampling distance in meters
dtm = ug.compute.calc_dtm_from_dsm(dsm_data, pixel_size, sampling_distance)  # Calculate the DEM
chm = dtm-dsm_data # subtract the two, and you have a Canopy Height Model too

Index calculations

You can use it to calculate a variety of indices from your imagery:

# assuming you already loaded your data as ortho:
import uavgeo as ug

savi  = ug.compute.calc_savi(bandstack = ortho, red_id=1, nir_id=4, l = 0.51)
savi.plot.imshow(cmap = "greens")

Implemented indices:

Based on the list from FieldImageR. With some additional indices added. They can be accesses through the uavgeo.compute module. All functions expect a bandstack, which is an xarray.DataArraywityh multiple bands asbands data. And the required bands ids, eg.: red_id=1. By default the functions rescale the output floats back to uint8 (0-255). This behaviour can be turned of with the rescale = False parameter.

Index	calc_indexname	Description	Formula	Related Traits	References
BI	calc_bi	Brightness Index	sqrt((RA^2+GA^2+B^2)/3)	Vegetation coverage, water content	Richardson and Wiegand (1977)
SCI	calc_sci	Soil Color Index	(R-G)/(R+G)	Soil color	Mathieu et al. (1998)
GLI	calc_gli	Green Leaf Index	(2 * G-R-B)/(2 * G+R+B)	Chlorophyll	Louhaichi et al. (2001)
HI	calc_hi	Hue Index	(2*R-G-B)/(G-B)	Soil color	Escadafal et al. (1994)
NGRDI	calc_ngrdi	Normalized Green Red Difference Index	(G-R)/(G+R)	Chlorophyll, biomass, water content	Tucker (1979)
SI	calc_si	Saturation Index	(R-B)/(R+B)	Soil color	Escadafal et al. (1994)
VARI	calc_vari	Visible Atmospherically Resistant Index	(G-R)/(G+R-B)	Canopy, biomass, chlorophyll	Gitelson et al. (2002)
HUE	calc_hue	Overall Hue Index#	atan(2*(B-G-R)/30.5*(G-R))	Soil color	Escadafal et al. (1994)
BGI	calc_bgi	Blue Green Pigment Index	B/G	Chlorophyll	Zarco-Tejada et al. (2005)
PSRI	calc_psri	Plant Senescence Reflectance Index	(R-G)/(RE)	Chlorophyll, LAI	Merzlyak et al. (1999)
NDVI	calc_ndvi	Normalized Difference Vegetation Index	(NIR-R)/(NIR+R)	Chlorophyll, nitrogen, maturity	Rouse et al. (1974)
GNDVI	calc_gndvi	Green Normalized Difference Vegetation Index	(NIR-G)/(NIR+G)	Chlorophyll, LAI, biomass, yield	Gitelson et al. (1996)
RVI	calc_rvi	Ratio Vegetation Index	NIR/R	Chlorophyll, LAI, nitrogen, protein content, water content	Pearson and Miller (1972)
NDRE	calc_ndre	Normalized Difference Red Edge Index	(NIR-RE)/(NIR+RE)	Biomass, water content, nitrogen	Gitelson and Merzlyak (1994)
TVI	calc_tvi	Triangular Vegetation Index	0.5 * (120 * (NIR — G)-200 * (R — G))	Chlorophyll	Broge and Leblanc (2000)
CVI	calc_cvi	Chlorophyll Vegetation Index	(NIR * R)/(GA^2)	Chlorophyll	Vincini et al. (2008)
EVI	calc_evi	Enhanced Vegetation Index	2.5 *(NIR — R)/(NIR + 6 * R — 7.5 * B)	Nitrogen, chlorophyll	Huete et al. (2002)
CIG	calc_cig	Chlorophyll Index — Green	(NIR/G) — 1	Chlorophyll	Gitelson et al. (2003)
CIRE	calc_cire	Chlorophyll Index — Red Edge	(NIR/RE) — 1	Chlorophyll	Gitelson et al. (2003)
DVI	calc_dvi	Difference Vegetation Index	NIR-RE	Nitrogen, chlorophyll	Jordan (1969)
RGBVI	calc_rgbvi	RGB Vegetation Index	((G^2)-(R * B)/(G^2)+(R * B))	Nitrogen, chlorophyll	Bendig et al. (2015)
SAVI	calc_savi	Soil Adjusted Vegetation Index	(NIR-R)/(NIR+R+l)*(1+l)	Vegetation coverage, LAI	Huete (1988)
-------	----------------	-------------	---------	----------------	------------
NDWI	calc_ndwi	Normalized Difference Water Index	(G-NIR)/(G+NIR)	Water coverage, water content	McFeeters (1996)
MNDWI	calc_mndwi	Modified Normalized Difference Water Index	(G-SWIR)/(GREEN+SWIR)	Water coverage, water content	McFeeters (1996)
AWEIsh	calc_aweish	Automated water extraction index (sh)	B + 2.5 * G - 1.5 * (NIR-SWIR1) - 0.25 * SWIR2	Water coverage, water content	Fayeisha (2014)
AWEInsh	calc_aweinsh	Automated water extraction index (nsh)	4 * (G - SWIR1) - (0.25 * NIR + 2.75* SWIR1)	Water coverage, water content	Fayeisha (2014)

Custom/other spectral index:

You could also write your own index calculators, according to the following template:

from uavgeo.compute import rescale_floats

def calc_custom(bandstack:xr.DataArray, band_a=1, band_b=2, rescale=True):
    
    ds_b = bandstack.astype(float)
    a: xr.DataArray = ds_b.sel(band=band_a)
    b: xr.DataArray = ds_b.sel(band=band_b)
    
    custom = a/b+1
    custom.name = "custom index"
    if rescale:
        custom = rescale_floats(custom)
    return custom

Shadow calculations:

Calculate vineyard shadows using a method proposed by Velez et al. (2021) for vineyards and UAVs. Based on Kmeans. Velez et al. (2021). "A New Leaf Area Index Methodology Based on Unmanned Aerial Vehicle Imagery for Vineyards." https://oeno-one.eu/article/view/4639

from uavgeo.compute import calc_vineyard_shadows

vineyard_data = xr.open_rasterio('path/to/vineyard_image.tif')
shadows_mask = calc_vineyard_shadows(vineyard_data)
# can also be used with a band_id to base the shadows from (NIR or Red are usally best)
shadows_mask = calc_vineyard_shadows(vineyard_data, band_id =4) 

Installation:

It is built upon the work of rioxarray, geopandas, shapely and a few more: see requirements.txt. Additionally, when working with the object detection part, the ultralytics and torch libraries (torch, torchvision, torchdata) is also a prerequisite. You can choose to install everything in a Python virtual environment or directly run a jupyterlab docker:

Option A: Setup directly in python:

0. Create a new environment (optional but recommended):

   conda create -n uavgeo_env python=3.10
   conda activate uavgeo_env
   

1. Install the required dependencies:

   Using conda (not recommended):

   conda install -c conda-forge rioxarray geopandas shapely
   

   Using pip:

   pip install -f rioxarray geopandas shapely
   

2. Install this package (for now: pip only)

       pip install uavgeo
   

Option B: Setup through Docker:

This starts a premade jupyter environment with everything preinstalled, based around a nvidia docker image for DL support.

• Linux/Ubuntu:

  docker run --rm -it --runtime=nvidia -p 8888:8888 --gpus 1 --shm-size=5gb --network=host -v /path_to_local/dir:/home/jovyan jurrain/drone-ml:gpu-torch11.8-uavgeoformers
  

--network=host flag whether you want to run it on a different machine in the same network, and want to access the notebook. (does not run locally)

-v flag makes sure that once downloaded, it stays in that folder, accessible from the PC, and when restarting, all the weights etc. remain in that folder. path_to_local/dir is thew path to your working dir where you want to access the notebook from. can be . if you already cded into it.

--runtime=nvidia can be skipped when working on WSL2

• Windows requires WSL2 and NVIDIA drivers, WSL2 should also have the nvidia toolkit (for deep learning)