Monthly Archives: October 2016

Above-Ground Biomass & Remote Sensing

Nice blog post giving a good introduction to AGB estimation using remote sensing. Wish it would have also mentioned the upcoming ESA BIOMASS radar mission.


Forest and Greenhouse Effect – Sink or Source?

Forests play an important role in maintaining the global carbon as they are the primary source of biomass which in turn contains a vast reserve of carbon dioxide, an important greenhouse gas. Of all terrestrial ecosystems, forests contain the largest store of carbon and have a large biomass per unit area. The main carbon pools in forests are plant biomass (above- and below-ground), coarse woody debris, litter and soil containing organic and inorganic carbon (Nizami et al, 2009). The ability of forests to both sequester and emit greenhouse gases coupled with ongoing widespread deforestation has resulted in forests and land-use change.

Since we were kids, we were all told in biology class that vegetation can absorb carbon dioxide from atmosphere, stored as organics and release oxygen through photosynthesis. In fact, there are other processes that we are not familiar with. The carbon which forest…

View original post 1,158 more words


DG Launches SpaceNet, Opening Access to Hi-Res Satellite Imagery for Deep Learning Research

DigitalGlobe has recently launched SpaceNet, an online repository of satellite imagery and associated training data, for users to experiment with machine learning and deep learning algorithms. Spacenet has been launched as a collaboration between DigitalGlobe, CosmiQ Works, and NVIDIA, and is available as a public dataset on Amazon Web Services (AWS). As a first step, SpaceNet will contain DigitalGlobe’s high resolution multispectral imagery from their premier WorldView-2 satellite at its industry-leading full 8-band spectral resolution and over 200,000 curated building footbrints across Rio de Janeiro, Brazil. This is unprecedented: Never before has satellite imagery at such high resolution of 50 cm been released publicly with building annotations. The released dataset contains over 7000 images over Rio de Janeiro. The satellite imagery is being delivered in GeoTIFF format while the building footprints are in GeoJSON format.


True color WV-2 high resolution imagery sample from the SpaceNet repository, along with corresponding building footprints. Source: NVIDIA

According to SpaceNet:

This dataset is being made public to advance the development of algorithms to automatically extract geometric features such as roads, building footprints, and points of interest using satellite imagery.

Scripts are already cropping up on GitHub for manipulating and using the satellite imagery data on SpaceNet: see code examples from Development Seed here and from CosmiQ Works here. NVIDIA has also released a detailed case study of analysis of SpaceNet data using their Deep Learning GPU Training System (DIGITS) platform, demonstrating the power and capability of GPU-based deep learning algorithms applied over high resolution satellite imagery. Application examples include detection of each building as a separate object and determining a bounding box around it, and semantic segmentation to partition the image into regions of pixels that can be given a common label, such as “building”, “forest”, “road”, or “water”.

SpaceNet plans a massive increase in both images and labeled features to be made available over the platform in the future. Incidentally, the name SpaceNet is inspired from ImageNet, a similar database of images created to help spur early advancements in computer vision.

To read more about the launch of SpaceNet, see coverage on GISCafeTechCrunch, MIT Technology Review, and Popular Science.

SpaceNet datasets can be accessed on AWS here.


MODIStsp: R Package for Analysing MODIS Time Series Data

An R package for automating the processing and analysis of MODIS Land Products raster datasets has recently been released by Lorenzo Busetto and Luigi Ranghetti at the Institute for Electromagnetic Sensing of  Environment, National Research Council of Italy (IREA-CNR). This package, called MODIStsp, is available for download on GitHub. It provides a user-friendly GUI, batch processing utilities, and access to source code for user modification and customisation. MODIStsp has the capability of performing several preprocessing steps (e.g. download, mosaicing, reprojection and resize) on MODIS products, and on-the-fly computation of time series of Spectral Indexes

For more details and package release information, please visit Spatial Processing in R blog and see the paper published in Computers & Geosciences journal.