Efficiently and automatically acquiring information on earthquake damage through remote sensing has posed great challenges because the classical methods of detecting houses damaged by destructive earthquakes are often both time consuming and low in accuracy. A series of deep-learning-based techniques have been developed and recent studies have demonstrated their high intelligence for automatic target extraction for natural and remote sensing images. For the detection of small artificial targets, current studies show that You Only Look Once (YOLO) has a good performance in aerial and Unmanned Aerial Vehicle (UAV) images. However, less work has been conducted on the extraction of damaged houses. In this study, we propose a YOLOv5s-ViT-BiFPN-based neural network for the detection of rural houses. Specifically, to enhance the feature information of damaged houses from the global information of the feature map, we introduce the Vision Transformer into the feature extraction network. Furthermore, regarding the scale differences for damaged houses in UAV images due to the changes in flying height, we apply the Bi-Directional Feature Pyramid Network (BiFPN) for multi-scale feature fusion to aggregate features with different resolutions and test the model. We took the 2021 Yangbi earthquake with a surface wave magnitude (Ms) of 6.4 in Yunan, China, as an example; the results show that the proposed model presents a better performance, with the average precision (AP) being increased by 9.31% and 1.23% compared to YOLOv3 and YOLOv5s, respectively, and a detection speed of 80 FPS, which is 2.96 times faster than YOLOv3. In addition, the transferability test for five other areas showed that the average accuracy was 91.23% and the total processing time was 4 min, while 100 min were needed for professional visual interpreters. The experimental results demonstrate that the YOLOv5s-ViT-BiFPN model can automatically detect damaged rural houses due to destructive earthquakes in UAV images with a good performance in terms of accuracy and timeliness, as well as being robust and transferable.

Ongoing climate change poses a major threat to the soil resources of many African countries that mainly rely on an agricultural economy. While arid and semi-arid lands (ASALs) take up most of Kenya’s land mass, approximately 64% of its total croplands lie within mountainous areas with high rainfall, hence, areas highly vulnerable to water erosion. Flooding of the Great Lakes and increasing desertification of the ASALs are illustrative cases of the implications of recent precipitation dynamics in Kenya. This study applied the Revised Universal Soil Loss Equation (RUSLE) to estimate future soil erosion rates at the national level based on four Coupled Model Intercomparison Project v5 (CMIP5) models under two Representative Concentration Pathway (RCP) scenarios. Results showed the current soil loss rate to be at 4.76 t ha⁻¹ yr⁻¹ and projected an increase in average rainfall erosivity under the two scenarios, except for RCP-2.6 (2030s) and (2080s) for the MIROC-5 model. Future projections revealed an incremental change in rainfall erosivity from the baseline climate by a cumulative average of 39.9% and 61.1% for all scenarios by the 2030s and 2080s, respectively, while soil loss is likely to increase concomitantly by 29% and 60%, respectively. The CCCMA_CANESM2 model under the RCP 8.5 (2080s) scenario projected the highest erosion rate of 15 t ha⁻¹ yr⁻¹ over Kenya, which is a maximum increase of above 200%, with the Rift Valley region recording an increase of up to 100% from 7.05 to 14.66 t ha⁻¹ yr⁻¹. As a first countrywide future soil erosion study, this assessment provides a useful reference for preventing water erosion and improving ecosystem service security.

The Kenya Great Rift Valley (KGRV) region unique landscape comprises of mountainous terrain, large valley-floor lakes, and agricultural lands bordered by extensive Arid and Semi-Arid Lands (ASALs). The East Africa (EA) region has received high amounts of rainfall in the recent past as evidenced by the rising lake levels in the GRV lakes. In Kenya, few studies have quantified soil loss at national scales and erosion rates information on these GRV lakes’ regional basins within the ASALs is lacking. This study used the Revised Universal Soil Loss Equation (RUSLE) model to estimate soil erosion rates between 1990 and 2015 in the Great Rift Valley region of Kenya which is approximately 84.5% ASAL. The mean erosion rates for both periods was estimated to be tolerable (6.26 t ha⁻¹ yr⁻¹ and 7.14 t ha⁻¹ yr⁻¹ in 1990 and 2015 respectively) resulting in total soil loss of 116 Mt yr⁻¹ and 132 Mt yr⁻¹ in 1990 and 2015 respectively. Approximately 83% and 81% of the erosive lands in KGRV fell under the low risk category (10 t ha⁻¹ yr⁻¹ in both years. The mountainous central parts of the KGRV with Andosol/Nitisols soils and high rainfall experienced a large change of land uses to croplands thus had highest soil loss net change (4.34 t ha⁻¹ yr⁻¹). In both years, forests recorded the lowest annual soil loss rates (<3.0 t ha⁻¹ yr⁻¹) while most of the ASAL districts presented erosion rates (<8 t ha⁻¹ yr⁻¹). Only 34% of all the protected areas were found to have erosion rates <10 t ha⁻¹ yr⁻¹ highlighting the need for effective anti-erosive measures.

Beira Lake, located in Colombo, Sri Lanka, has suffered severe anthropogenic impacts, with previous restoration attempts failing due to a limited understanding of pollutant dynamics. Aiming to fill this gap, a comprehensive study was conducted during dry and wet seasons to assess the spatiotemporal water pollution of Beira Lake, employing key physicochemical parameters, numerical indices, and remote sensing analysis. The water pollution index (WPI) results categorize Beira Lake as highly polluted, with WPI values ranging from 2.38 ± 0.92 in the wet season to 2.53 ± 1.32 in the dry season. Comparatively higher COD levels recorded in the Beira Lake network, especially for Gangarama Lake show significant pollution levels during both the dry and wet seasons, e.g., the highest COD levels, at 306.40 mg/L, were observed during the wet season. The Trophic State Index (TSI) results indicate eutrophic and hypereutrophic conditions in Beira Lake, which are particularly pronounced during the wet season. The heavy metal pollution index (HPI) results suggest elevated heavy metal concentrations in Beira Lake, especially in the wet season. Combined with field investigation results, a remote sensing data analysis between 2016 and 2023 reveals significant improvements in water transparency, suggesting positive effects of recent management interventions. Parameters demanding attention include COD, nitrate, and total phosphate levels due to their consistent exceedance of permissible limits. The PCA results of indices correlations between wet and dry seasons offer valuable insights into the complex dynamics of Beira Lake’s water quality. The study makes recommendations for restoring Beira Lake, including stringent pollution controls, regular dredging, green infrastructure implementation, implementing new rules and regulations, and community engagement.