As the basic feature of building, building edges play an important role in many fields such as urbanization monitoring, city planning, surveying and mapping. Building edges detection from high spatial resolution remote sensing (HSRRS) imagery has always been a long-standing problem. Inspired by the recent success of deep-learning-based edge detection, a building edge detection model using a richer convolutional features (RCF) network is employed in this paper to detect building edges. Firstly, a dataset for building edges detection is constructed by the proposed most peripheral constraint conversion algorithm. Then, based on this dataset the RCF network is retrained. Finally, the edge probability map is obtained by RCF-building model, and this paper involves a geomorphological concept to refine edge probability map according to geometric morphological analysis of topographic surface. The experimental results suggest that RCF-building model can detect building edges accurately and completely, and that this model has an edge detection F-measure that is at least 5% higher than that of other three typical building extraction methods. In addition, the ablation experiment result proves that using the most peripheral constraint conversion algorithm can generate more superior dataset, and the involved refinement algorithm shows a higher F-measure and better visual effect contrasted with the non-maximal suppression algorithm.

Conventional geographic object-based image analysis (GEOBIA) land cover classification methods by using very high resolution images are hardly applicable due to their complex ground truth and manually selected features, while convolutional neural networks (CNNs) with many hidden layers provide the possibility of extracting deep features from very high resolution images. Compared with pixel-based CNNs, superpixel-based CNN classification, carrying on the idea of GEOBIA, is more efficient. However, superpixel-based CNNs are still problematic in terms of their processing units and accuracies. Firstly, the limitations of salt and pepper errors and low boundary adherence caused by superpixel segmentation still exist; secondly, this method uses the central point of the superpixel as the classification benchmark in identifying the category of the superpixel, which does not allow classification accuracy to be ensured. To solve such problems, this paper proposes a region-based majority voting CNN which combines the idea of GEOBIA and the deep learning technique. Firstly, training data was manually labeled and trained; secondly, images were segmented under multiresolution and the segmented regions were taken as basic processing units; then, point voters were generated within each segmented region and the perceptive fields of points voters were put into the multi-scale CNN to determine their categories. Eventually, the final category of each region was determined in the region majority voting system. The experiments and analyses indicate the following: 1. region-based majority voting CNNs can fully utilize their exclusive nature to extract abstract deep features from images; 2. compared with the pixel-based CNN and superpixel-based CNN, the region-based majority voting CNN is not only efficient but also capable of keeping better segmentation accuracy and boundary fit; 3. to a certain extent, region-based majority voting CNNs reduce the impact of the scale effect upon large objects; and 4. multi-scales containing small scales are more applicable for very high resolution image classification than the single scale.

The Africa Sahel-Sudan region, defined by annual rainfall between 150 and 1200 mm. Understanding the mechanism of vegetation response to water availability could help mitigate the potential adverse effects of climate change on global dryland ecosystems. In the Sahel-Sudan region, spatio-temporal changes and drivers of the vegetation-water response remain unclear. This study employs long-term satellite water and vegetation products as proxies of water availability and vegetation productivity to analyze changes in vegetation-water sensitivity and the cumulative effect duration (CED) representing a measure of the legacy effect of the impact of water constraints on vegetation. A random forest model was subsequently used to analyze potential climatic drivers of the observed vegetation response. During 1982–2016 we found a significant decrease (p < 0.05) in the sensitivity of vegetation productivity to water constraints in 26% of the Sahel-Sudan region, while 9% of the area showed a significantly increased sensitivity, mainly in the sub-humid zone. We further showed that CED significantly increased and decreased, respectively in around 9% of the study area in both cases. Our climatic driver attribution analysis suggested the existence of varying underlying mechanisms governing vegetation productivity in response to water deficit across the Sahel-Sudan dryland ecosystems. Our findings emphasize the need for diverse strategies in sustainable ecosystem management to effectively address these varying mechanisms.