A general deep learning model for bird detection in high-resolution airborne imagery.

Advances in artificial intelligence for computer vision hold great promise for increasing the scales at which ecological systems can be studied. The distribution and behavior of individuals is central to ecology, and computer vision using deep neural networks can learn to detect individual objects in imagery. However, developing supervised models for ecological monitoring is challenging because it requires large amounts of human-labeled training data, requires advanced technical expertise and computational infrastructure, and is prone to overfitting. This limits application across space and time. One solution is developing generalized models that can be applied across species and ecosystems. Using over 250,000 annotations from 13 projects from around the world, we develop a general bird detection model that achieves over 65% recall and 50% precision on novel aerial data without any local training despite differences in species, habitat, and imaging methodology. Fine-tuning this model with only 1000 local annotations increases these values to an average of 84% recall and 69% precision by building on the general features learned from other data sources. Retraining from the general model improves local predictions even when moderately large annotation sets are available and makes model training faster and more stable. Our results demonstrate that general models for detecting broad classes of organisms using airborne imagery are achievable. These models can reduce the effort, expertise, and computational resources necessary for automating the detection of individual organisms across large scales, helping to transform the scale of data collection in ecology and the questions that can be addressed.

Towards Synoptic Water Monitoring Systems: A Review of AI Methods for Automating Water Body Detection and Water Quality Monitoring Using Remote Sensing.

Water features (e.g., water quantity and water quality) are one of the most important environmental factors essential to improving climate-change resilience. Remote sensing (RS) technologies empowered by artificial intelligence (AI) have become one of the most demanded strategies to automating water information extraction and thus intelligent monitoring. In this article, we provide a systematic review of the literature that incorporates artificial intelligence and computer vision methods in the water resources sector with a focus on intelligent water body extraction and water quality detection and monitoring through remote sensing. Based on this review, the main challenges of leveraging AI and RS for intelligent water information extraction are discussed, and research priorities are identified. An interactive web application designed to allow readers to intuitively and dynamically review the relevant literature was also developed.

Urban Vegetation Cover and Vegetation Change in Accra, Ghana: Connection to Housing Quality.

The objectives are to (1) quantify, map, and analyze vegetation cover distributions and changes across Accra, Ghana, for 2002 and 2010; and (2) examine the statistical relationship between vegetation cover and a housing quality index (HQI) for 2000 at the neighborhood level. Pixel-level vegetation cover maps derived using threshold classification of 2002 and 2010 QuickBird normalized difference vegetation index images have very high overall accuracies and yield an estimate of 5.9 percent vegetation cover reduction over the study area between 2002 and 2010. A high degree of variance in vegetation cover for individual dates is explained by HQI at the neighborhood level, although minimal covariability between absolute or relative vegetation cover change and HQI for 2000 was observed.

Frequency distribution signatures and classification of within-object pixels.

The premise of geographic object-based image analysis (GEOBIA) is that image objects are composed of aggregates of pixels that correspond to earth surface features of interest. Most commonly, image-derived objects (segments) or objects associated with predefined land units (e.g., agricultural fields) are classified using parametric statistical characteristics (e.g., mean and standard deviation) of the within-object pixels. The objective of this exploratory study was to examine the between- and within-class variability of frequency distributions of multispectral pixel values, and to evaluate a quantitative measure and classification rule that exploits the full pixel frequency distribution of within object pixels (i.e., histogram signatures) compared to simple parametric statistical characteristics. High spatial resolution Quickbird satellite multispectral data of Accra, Ghana were evaluated in the context of mapping land cover and land use and socioeconomic status. Results show that image objects associated with land cover and land use types can have characteristic, non-normal frequency distributions (histograms). Signatures of most image objects tended to match closely the training signature of a single class or sub-class. Curve matching approaches to classifying multi-pixel frequency distributions were found to be slightly more effective than standard statistical classifiers based on a nearest neighbor classifier.

The effect of input data transformations on object-based image analysis.

The effect of using spectral transform images as input data on segmentation quality and its potential effect on products generated by object-based image analysis are explored in the context of land cover classification in Accra, Ghana. Five image data transformations are compared to untransformed spectral bands in terms of their effect on segmentation quality and final product accuracy. The relationship between segmentation quality and product accuracy is also briefly explored. Results suggest that input data transformations can aid in the delineation of landscape objects by image segmentation, but the effect is idiosyncratic to the transformation and object of interest.

Geographic object-based delineation of neighborhoods of Accra, Ghana using QuickBird satellite imagery.

The objective was to test GEographic Object-based Image Analysis (GEOBIA) techniques for delineating neighborhoods of Accra, Ghana using QuickBird multispectral imagery. Two approaches to aggregating census enumeration areas (EAs) based on image-derived measures of vegetation objects were tested: (1) merging adjacent EAs according to vegetation measures and (2) image segmentation. Both approaches exploit readily available functions within commercial GEOBIA software. Image-derived neighborhood maps were compared to a reference map derived by spatial clustering of slum index values (from census data), to provide a relative assessment of potential map utility. A size-constrained iterative segmentation approach to aggregation was more successful than standard image segmentation or feature merge techniques. The segmentation approaches account for size and shape characteristics, enabling more realistic neighborhood boundaries to be delineated. The percentage of vegetation patches within each EA yielded more realistic delineation of potential neighborhoods than mean vegetation patch size per EA.