LDL (Landscape Digital Library) : A Digital Photographic Database of a Case Study Area in the River Po Valley, Northern Italy

Landscapes are both a synthesis and an expression of national, regional and local cultural heritages. It is therefore very important to develop techniques aimed at cataloguing and archiving their forms. This paper discusses the LDL (Landscape Digital Library) project, a Web accessible database that can present the landscapes of a territory with documentary evidence in a new format and from a new perspective. The method was tested in a case study area of the river Po valley (Northern Italy). The LDL is based on a collection of photographs taken following a systematic grid of survey points identified through topographic cartography; the camera level is that of the human eye. This methodology leads to an innovative landscape archive that differs from surveys carried out through aerial photographs or campaigns aimed at selecting "relevant" points of interest. Further developments and possible uses of the LDL are also discussed

A global view of shifting cultivation: Recent, current, and future extent

Mosaic landscapes under shifting cultivation, with their dynamic mix of managed and natural land covers, often fall through the cracks in remote sensing–based land cover and land use classifications, as these are unable to adequately capture such landscapes’ dynamic nature and complex spectral and spatial signatures. But information about such landscapes is urgently needed to improve the outcomes of global earth system modelling and large-scale carbon and greenhouse gas accounting. This study combines existing global Landsat-based deforestation data covering the years 2000 to 2014 with very high-resolution satellite imagery to visually detect the specific spatio-temporal pattern of shifting cultivation at a one-degree cell resolution worldwide. The accuracy levels of our classification were high with an overall accuracy above 87%. We estimate the current global extent of shifting cultivation and compare it to other current global mapping endeavors as well as results of literature searches. Based on an expert survey, we make a first attempt at estimating past trends as well as possible future trends in the global distribution of shifting cultivation until the end of the 21st century. With 62% of the investigated one-degree cells in the humid and sub-humid tropics currently showing signs of shifting cultivation—the majority in the Americas (41%) and Africa (37%)—this form of cultivation remains widespread, and it would be wrong to speak of its general global demise in the last decades. We estimate that shifting cultivation landscapes currently cover roughly 280 million hectares worldwide, including both cultivated fields and fallows. While only an approximation, this estimate is clearly smaller than the areas mentioned in the literature which range up to 1,000 million hectares. Based on our expert survey and historical trends we estimate a possible strong decrease in shifting cultivation over the next decades, raising issues of livelihood security and resilience among people currently depending on shifting cultivation

Relating visual and semantic image descriptors

This paper addresses the automatic analysis of visual content and extraction of metadata beyond pure visual descriptors. Two approaches are described: Automatic Image Annotation (AIA) and Confidence Clustering (CC). AIA attempts to automatically classify images based on two binary classifiers and is designed for the consumer electronics domain. Contrastingly, the CC approach does not attempt to assign a unique label to images but rather to organise the database based on concepts

Visualising Basins of Attraction for the Cross-Entropy and the Squared Error Neural Network Loss Functions

Quantification of the stationary points and the associated basins of attraction of neural network loss surfaces is an important step towards a better understanding of neural network loss surfaces at large. This work proposes a novel method to visualise basins of attraction together with the associated stationary points via gradient-based random sampling. The proposed technique is used to perform an empirical study of the loss surfaces generated by two different error metrics: quadratic loss and entropic loss. The empirical observations confirm the theoretical hypothesis regarding the nature of neural network attraction basins. Entropic loss is shown to exhibit stronger gradients and fewer stationary points than quadratic loss, indicating that entropic loss has a more searchable landscape. Quadratic loss is shown to be more resilient to overfitting than entropic loss. Both losses are shown to exhibit local minima, but the number of local minima is shown to decrease with an increase in dimensionality. Thus, the proposed visualisation technique successfully captures the local minima properties exhibited by the neural network loss surfaces, and can be used for the purpose of fitness landscape analysis of neural networks.Comment: Preprint submitted to the Neural Networks journa

Landscape preferences, ecological quality and biodiversity protection

The loss of biological diversity is a major environmental problem occurring on a global scale. Human-environment researchers have an important role in shaping policy and programs at a local, national and international level. This paper explores human preference for landscapes relative to ecological quality and assesses the relationship between these preferences and land management behavior. A survey of more than 1000 urban and rural residents of southeastern Australia examined preferences for 36 black and white photographs of native vegetation. There was more commonality than difference between urban and rural preference for different arrays of native vegetation. Preference for Eucalyptus species was higher than preference for non-Eucalyptus species. Preference ratings indicate minimal differences across landscapes with distinct variation in ecological quality. The study suggests that preference for landscapes of relatively high ecological quality is associated with behavior that is protective of this resource

PlaNet - Photo Geolocation with Convolutional Neural Networks

Is it possible to build a system to determine the location where a photo was taken using just its pixels? In general, the problem seems exceptionally difficult: it is trivial to construct situations where no location can be inferred. Yet images often contain informative cues such as landmarks, weather patterns, vegetation, road markings, and architectural details, which in combination may allow one to determine an approximate location and occasionally an exact location. Websites such as GeoGuessr and View from your Window suggest that humans are relatively good at integrating these cues to geolocate images, especially en-masse. In computer vision, the photo geolocation problem is usually approached using image retrieval methods. In contrast, we pose the problem as one of classification by subdividing the surface of the earth into thousands of multi-scale geographic cells, and train a deep network using millions of geotagged images. While previous approaches only recognize landmarks or perform approximate matching using global image descriptors, our model is able to use and integrate multiple visible cues. We show that the resulting model, called PlaNet, outperforms previous approaches and even attains superhuman levels of accuracy in some cases. Moreover, we extend our model to photo albums by combining it with a long short-term memory (LSTM) architecture. By learning to exploit temporal coherence to geolocate uncertain photos, we demonstrate that this model achieves a 50% performance improvement over the single-image model

Calculating the inherent visual structure of a landscape (inherent viewshed) using high-throughput computing

This paper describes a method of calculating the inherent visibility at all locations in a landscape (‘total viewshed’) by making use of redundant computer cycles. This approach uses a simplified viewshed program that is suitable for use within a distributed environment, in this case managed by the Condor system. Distributing the calculation in this way reduced the calculation time of our example from an estimated 34 days to slightly over 25 hours using a cluster of 43 workstations. Finally, we discuss the example ‘total viewshed’ raster for the Avebury region, and briefly highlight some of its implications