Technology is Set to Change Real Estate Forever

Major developments in the technological environment can become commonplace very quickly. They are now impacting upon a broad range of information-based service sectors, as high growth Internet-based firms, such as Google, Amazon, Facebook and Airbnb, and financial technology (Fintech) start-ups expand their product portfolios into new markets. Real estate is one of the information-based service sectors that is currently being impacted by this new type of competitor and the broad range of disruptive digital technologies that have emerged. Due to the vast troves of data that these Internet firms have at their disposal and their asset-light (cloud-based) structures, they are able to offer highly-targeted products at much lower costs than conventional brick-and-mortar companies.

Mapping of Submerged Aquatic Vegetation in Rivers From Very High Resolution Image Data, Using Object Based Image Analysis Combined with Expert Knowledge

The use of remote sensing for monitoring of submerged aquatic vegetation (SAV) in fluvial environments has been limited by the spatial and spectral resolution of available image data. The absorption of light in water also complicates the use of common image analysis methods. This paper presents the results of a study that uses very high resolution (VHR) image data, collected with a Near Infrared sensitive DSLR camera, to map the distribution of SAV species for three sites along the Desselse Nete, a lowland river in Flanders, Belgium. Plant species, including Ranunculus aquatilis L., Callitriche obtusangula Le Gall, Potamogeton natans L., Sparganium emersum L. and Potamogeton crispus L., were classified from the data using Object-Based Image Analysis (OBIA) and expert knowledge. A classification rule set based on a combination of both spectral and structural image variation (e.g. texture and shape) was developed for images from two sites. A comparison of the classifications with manually delineated ground truth maps resulted for both sites in 61% overall accuracy. Application of the rule set to a third validation image, resulted in 53% overall accuracy. These consistent results show promise for species level mapping in such biodiverse environments, but also prompt a discussion on assessment of classification accuracy.