Explore the vast range of titles available.

\"For fans of Aleksandra Mizielinska and Daniel Mizielinska's Maps, and our own Around the World series, is this illustrated map book that explores the neighborhoods of 16 kids around the world. Based on real kids and their families, the book highlights significant places in the community through the child's eyes, such as where they live, where they go to school and their favorite places to play. Where We Live is filled with fascinating stories that depict what real life is like for kids from every corner of the globe. Bruno in Antarctica has to climb out the window when the snow mound blocks his front door. Namisha lives in a small village in Zambia and has to stay home from school whenever there's a hippo sighting. And in the evenings, Anani in Ethiopia helps his mother with the coffee ceremony at their home, where the community gathers. Author Margriet Ruurs is a frequent guest speaker at international schools and an avid world traveler. The stories featured in the book are based on people she has met on her travels. The end matter includes an author's note, activities to help kids make connections between the children in the book and to their own lives, a glossary and an index. Where We Live is both a valuable resource for learning about global cultures and an insightful look at how much kids around the world have in common.\"-- Provided by publisher.

Spatially explicit, fine-grained datasets describing historical urban extents are rarely available prior to the era of operational remote sensing. However, such data are necessary to better understand long-term urbanization and land development processes and for the assessment of coupled nature–human systems (e.g., the dynamics of the wildland–urban interface). Herein, we propose a framework that jointly uses remote-sensing-derived human settlement data (i.e., the Global Human Settlement Layer, GHSL) and scanned, georeferenced historical maps to automatically generate historical urban extents for the early 20th century. By applying unsupervised color space segmentation to the historical maps, spatially constrained to the urban extents derived from the GHSL, our approach generates historical settlement extents for seamless integration with the multi-temporal GHSL. We apply our method to study areas in countries across four continents, and evaluate our approach against historical building density estimates from the Historical Settlement Data Compilation for the US (HISDAC-US), and against urban area estimates from the History Database of the Global Environment (HYDE). Our results achieve Area-under-the-Curve values >0.9 when comparing to HISDAC-US and are largely in agreement with model-based urban areas from the HYDE database, demonstrating that the integration of remote-sensing-derived observations and historical cartographic data sources opens up new, promising avenues for assessing urbanization and long-term land cover change in countries where historical maps are available.

Understanding the spatiotemporal dynamics of global urbanization over a long time series is increasingly important for sustainable development goals. The harmonized nighttime light (NTL) time-series composites created by fusing multi-source NTL observations provide a long and consistent record of the nightscape for characterizing and understanding global urban dynamics. In this study, we generated a global dataset of annual urban extents (1992–2020) using consistent NTL observations and analyzed the spatiotemporal patterns of global urban dynamics over nearly 30 years. The urbanized areas associated with locally high intensity human activities were mapped from the global NTL time-series imagery using a new stepwise-partitioning framework. This framework includes three components: (1) clustering of NTL signals to generate potential urban clusters, (2) identification of optimal thresholds to delineate annual urban extents, and (3) check of temporal consistency to correct pixel-level urban dynamics. We found that the global urban land area percentage of the Earth's land surface rose from 0.22 % to 0.69 % between 1992 and 2020. Urban dynamics over the past 3 decades at the continent, country, and city levels exhibit various spatiotemporal patterns. Our resulting global urban extents (1992–2020) were evaluated using other urban remote sensing products and socioeconomic data. The evaluations indicate that this dataset is reliable for characterizing spatial extents associated with intensive human settlement and high-intensity socioeconomic activities. The dataset of global urban extents from this study can provide unique information to capture the historical and future trajectories of urbanization and to understand and tackle urbanization impacts on food security, biodiversity, climate change, and public well-being and health. This dataset can be downloaded from https://doi.org/10.6084/m9.figshare.16602224.v1 (Zhao et al., 2021).

Spatially consistent and up-to-date maps of human settlements are crucial for addressing policies related to urbanization and sustainability, especially in the era of an increasingly urbanized world. The availability of open and free Sentinel-2 data of the Copernicus Earth Observation program offers a new opportunity for wall-to-wall mapping of human settlements at a global scale. This paper presents a deep-learning-based framework for a fully automated extraction of built-up areas at a spatial resolution of 10 m from a global composite of Sentinel-2 imagery. A multi-neuro modeling methodology building on a simple Convolution Neural Networks architecture for pixel-wise image classification of built-up areas is developed. The core features of the proposed model are the image patch of size 5 × 5 pixels adequate for describing built-up areas from Sentinel-2 imagery and the lightweight topology with a total number of 1,448,578 trainable parameters and 4 2D convolutional layers and 2 flattened layers. The deployment of the model on the global Sentinel-2 image composite provides the most detailed and complete map reporting about built-up areas for reference year 2018. The validation of the results with an independent reference dataset of building footprints covering 277 sites across the world establishes the reliability of the built-up layer produced by the proposed framework and the model robustness. The results of this study contribute to cutting-edge research in the field of automated built-up areas mapping from remote sensing data and establish a new reference layer for the analysis of the spatial distribution of human settlements across the rural–urban continuum.

There is a growing demand for detailed and accurate landslide maps and inventories around the globe, but particularly in hazard-prone regions such as the Himalayas. Most standard mapping methods require expert knowledge, supervision and fieldwork. In this study, we use optical data from the Rapid Eye satellite and topographic factors to analyze the potential of machine learning methods, i.e., artificial neural network (ANN), support vector machines (SVM) and random forest (RF), and different deep-learning convolution neural networks (CNNs) for landslide detection. We use two training zones and one test zone to independently evaluate the performance of different methods in the highly landslide-prone Rasuwa district in Nepal. Twenty different maps are created using ANN, SVM and RF and different CNN instantiations and are compared against the results of extensive fieldwork through a mean intersection-over-union (mIOU) and other common metrics. This accuracy assessment yields the best result of 78.26% mIOU for a small window size CNN, which uses spectral information only. The additional information from a 5 m digital elevation model helps to discriminate between human settlements and landslides but does not improve the overall classification accuracy. CNNs do not automatically outperform ANN, SVM and RF, although this is sometimes claimed. Rather, the performance of CNNs strongly depends on their design, i.e., layer depth, input window sizes and training strategies. Here, we conclude that the CNN method is still in its infancy as most researchers will either use predefined parameters in solutions like Google TensorFlow or will apply different settings in a trial-and-error manner. Nevertheless, deep-learning can improve landslide mapping in the future if the effects of the different designs are better understood, enough training samples exist, and the effects of augmentation strategies to artificially increase the number of existing samples are better understood.

Event-based landslide inventories are essential sources to broaden our understanding of the causal relationship between triggering events and the occurring landslides. Moreover, detailed inventories are crucial for the succeeding phases of landslide risk studies like susceptibility and hazard assessment. The openly available inventories differ in the quality and completeness levels. Event-based landslide inventories are created based on manual interpretation, and there can be significant differences in the mapping preferences among interpreters. To address this issue, we used two different datasets to analyze the potential of U-Net and machine learning approaches for automated landslide detection in the Himalayas. Dataset-1 is composed of five optical bands from the RapidEye satellite imagery. Dataset-2 is composed of the RapidEye optical data, and ALOS-PALSAR derived topographical data. We used a small dataset consisting of 239 samples acquired from several training zones and one testing zone to evaluate our models’ performance using the fully convolutional U-Net model, Support Vector Machines (SVM), K-Nearest Neighbor, and the Random Forest (RF). We created thirty-two different maps to evaluate and understand the implications of different sample patch sizes and their effect on the accuracy of landslide detection in the study area. The results were then compared against the manually interpreted inventory compiled using fieldwork and visual interpretation of the RapidEye satellite image. We used accuracy assessment metrics such as F1-score, Precision, Recall, and Mathews Correlation Coefficient (MCC). In the context of the Nepali Himalayas, employing RapidEye images and machine learning models, a viable patch size was investigated. The U-Net model trained with 128 × 128 pixel patch size yields the best MCC results (76.59%) with the dataset-1. The added information from the digital elevation model benefited the overall detection of landslides. However, it does not improve the model’s overall accuracy but helps differentiate human settlement areas and river sand bars. In this study, the U-Net achieved slightly better results than other machine learning approaches. Although it can depend on architecture of the U-Net model and the complexity of the geographical features in the imagery, the U-Net model is still preliminary in the domain of landslide detection. There is very little literature available related to the use of U-Net for landslide detection. This study is one of the first efforts of using U-Net for landslide detection in the Himalayas. Nevertheless, U-Net has the potential to improve further automated landslide detection in the future for varied topographical and geomorphological scenes.

Slums are a structural feature of urbanization, and shifting urbanization trends underline their significance for the cities of tomorrow. Despite their importance, data and knowledge on slums are very limited. In consideration of the current data landscape, it is not possible to answer one of the most essential questions: Where are slums located? The goal of this study is to provide a more nuanced understanding of the geography of slums and their growth trajectories. The methods rely on the combination of different datasets (city-level slum maps, world cities, global human settlements layer, Atlas of Informality). Slum data from city-level maps form the backbone of this research and are made compatible by differentiating between the municipal area, the urbanized area, and the area beyond. This study quantifies the location of slums in 30 cities, and our findings show that only half of all slums are located within the administrative borders of cities. Spatial growth has also shifted outwards. However, this phenomenon is very different in different regions of the world; the municipality captures less than half of all slums in Africa and the Middle East but almost two-thirds of all slums in cities of South Asia. These insights are used to estimate land requirements within the Sustainable Development Goals time frame. In 2015, almost one billion slum residents occupied a land area as large as twice the size of the country of Portugal. The estimated 380 million residents to be added up to 2030 will need land equivalent to the size of the country of Egypt. This land will be added to cities mainly outside their administrative borders. Insights are provided on how this land demand differs within cities and between world regions. Such novel insights are highly relevant to the policy actions needed to achieve Target 11.1 of the Sustainable Development Goals (“by 2030, ensure access for all to adequate, safe and affordable housing and basic services, and upgrade slums”) as interventions targeted at slums or informal settlements are strongly linked to political and administrative boundaries. More research is needed to draw attention to the urban expansion of cities and the role of slums and informal settlements.

Flooding is one of the most devastating natural disasters in the world that causes massive damages to natural and man-made features every year. As urbanization continues at an unprecedented rate, the damage caused by such natural disasters keeps increasing. Moreover, as human population continues to explode, settlements keep expanding and human activities continue to grow in low-lying areas vulnerable to flooding activities. Different strategies have been adopted to prevent flood hazards. One such strategy is flood susceptibility mapping to identify vulnerable areas prone to flooding. Such mapping processes are important for early warning system, emergency services, prevention and mitigation of future floods and implementation of flood management strategies. Harris County is a rapidly growing metropolitan area, situated on a low-lying coastal plain with little topographic relief and high susceptibility to flooding. This study incorporates a GIS based weighted multi-criteria analysis to determine flood prone areas in Harris County, Texas by integrating nine flood conditioning factors such as slope, elevation, soil type, rainfall intensity, flow accumulation, LULC, NDVI and distance from river and distance from road. The objectives of this study is threefold. Firstly, to determine the impact weight of the selected nine flood conditioning factors. Secondly, to combine the conditioning factors using weighted overlay method in ArcGIS to map the areas in Harris County that are prone to flooding. And finally, to overlay the 2017 FEMA flood hazard map on the weighted overlay flood hazard map.

Human use of the land (for agriculture and settlements) has a substantial negative effect on biodiversity globally. However, not all species are adversely affected by land use, and indeed, some benefit from the creation of novel habitat. Geographically rare species may be more negatively affected by land use than widespread species, but data limitations have so far prevented global multi-clade assessments of land-use effects on narrow-ranged and widespread species. We analyse a large, global database to show consistent differences in assemblage composition. Compared with natural habitat, assemblages in disturbed habitats have more widespread species on average, especially in urban areas and the tropics. All else being equal, this result means that human land use is homogenizing assemblage composition across space. Disturbed habitats show both reduced abundances of narrow-ranged species and increased abundances of widespread species. Our results are very important for biodiversity conservation because narrow-ranged species are typically at higher risk of extinction than widespread species. Furthermore, the shift to more widespread species may also affect ecosystem functioning by reducing both the contribution of rare species and the diversity of species' responses to environmental changes among local assemblages.

The present study documents the landuse and landcover change on a stretch of the Bhagirathi river in the last three decades in response to river bank erosion. Landuse and landcover maps were prepared with the help of supervised image classification techniques and validated through accuracy assessment by kappa statistics. Landuse transformation matrix was prepared for different periods to understand the nature of landuse change in the study area. Simulation and prediction of LULC for the year 2035 was done to explore future riverine landscape dynamics based on Cellular Automata-Artificial Neural Network (CA-ANN) model through MOLUSCE (Modules of Landuse Change Evaluation) plugin of QGIS software. The kappa statistics coefficients are 0.85, 0.83 and 0.93 for the years 1990, 2005 and 2019 respectively. The result of the investigation reveals that during the last three decades (1990–2019) area under agricultural land and settlement increased by an annual rate of 10.20 and 1.03 km2 respectively, while area under fallow land, vegetation cover, water body and wetland shows a decreasing trend. Likewise, the simulation result of CA reveals that area under agriculture and settlement will continue to increase in future at the cost of other LULC classes like vegetation and fallow land. The Kappa overall, kappa histogram and kappa local values were 0.78, 0.92 and 0.85 respectively with 84.71% of correctness. This validates the prediction results that the area under agricultural land and settlement will increase at an annual rate of 2.65 km2 and 0.82 km2 respectively due to increase in accretional lands and population in the study area. The transformation of the area under vegetation and fallow land to agricultural practices and the unplanned human settlement in the riparian area indicate an alarming future scenario as far as loss of human life and property is concerned because the bank erosion is occurring in both the banks. Therefore, the study regarding the LULC change and its future scenario in the erosion affected floodplain will give a holistic understanding the land and landuse dynamics, for proper planning and management of the situation for the sustainability of the floodplains and its occupants.