Jiaozuo, located in the northwest of Henan Province, is one of the six major anthracite production bases in China. It is susceptible to land subsidence due to over a hundred years of mining history, continuous urbanization, frequent human activities, etc., which poses a great threat to urban infrastructure construction and people’s production and lives. However, traditional leveling techniques are not sufficient for monitoring large areas of land subsidence due to the time-consuming, labor-intensive, and expensive nature of the process. Furthermore, the results of conventional methods may not be timely, rendering them ineffective for monitoring purposes. With the continuous advancement of urbanization, land subsidence caused by groundwater extraction, ground load, and other factors in daily life poses a great threat to urban infrastructure construction and people’s production and lives. In order to monitor the land subsidence in the area of Jiaozuo city, this article uses the Sentienl-1A satellite data covering the city from March 2017 to March 2021 to obtain the accumulated land subsidence and the average land subsidence rate based on the Small Baselines Subset InSAR (SBAS-InSAR) technology. The results indicate that the surface of Jiaozuo area is generally stable, and there has been no large-scale settlement. The settlement rate is roughly between −1 mm/a and 2.2 mm/a, and the areas with obvious land subsidence are mainly located in the southeast and east of Jiaozuo city center. After field investigation, it was found that the land subsidence is mainly caused by two reasons: groundwater excessive mining and excessive surface load. In the northeast of Jiaozuo city, there is a certain uplift area. After on-site investigation, it was found that the area is connected to a tailings pond of an aluminum mine, constantly accumulating abandoned rock masses and sediment, causing an annual uplift rate of +6~+ 24 mm/a. The large-scale extraction of groundwater from farmland in the urban–rural integration area for irrigation of wheat has led to the settlement of buildings in the area with a rate of −11–−74 mm/a.

Mining can lead to overburden failure and ground damage, which are more severe in mining shallow buried thick coal seams (SBTCS) under thick aeolian sand (TAS). We attempted to discover characteristics of mining in this particular geological condition through theoretical derivation and numerical simulation, and field monitoring. Theoretical methods, combined with numerical simulation and field monitoring methods, reveal the essence of the development and distribution of surface cracks caused by mining SBTCS and depth to thickness ratio (DTR) to be 13.43, less than 15. The findings show that, when mining SBTCS, the overburden breaks down periodically, the initial collapse distance is greater than the collapse step, approximately 55 m on average, and the collapse step is approximately 45 m, on average, in the Daliuta Coal Mine. The collapsed blocks are stacked into goaf and form “masonry beams”, and many cracks and pores are generated between the blocks. The weak stress of the aeolian sand layer causes the movement angle in the aeolian sand layer to be smaller than that in the bedrock, and leads to much sheer, tension and compression failure on the ground, and the main forms of cracks are compression uplift, tensile cracking, shear step.

In Africa, the distribution of color steel tile roof (CSTR) can reflect the living standard of residents, and the analysis of its temporal and spatial changes can reflect the local changes in local living conditions. It is helpful to analyze the change of the local economic level. By using the satellite remote sensing image processing method to obtain the temporal and spatial change characteristics of CSTR and to analyze the changes in residents’ living conditions in Munyaka, Eldoret, Kenya, Africa, the model of multifeature decision tree method (DTM) extraction was established. The Normalized Difference Vegetation Index (NDVI) and Normalized Difference Building Index (NDBI) were used to remove farmland from the difference of the CSTR. The Normalized Difference Surface Index (NCSI) was constructed, and the texture features were analyzed to eliminate wasteland and bare land, respectively. The research results show that the Kappa coefficient is 0.9223, and the user precision and mapping precision are 97.79% and 91.10%, respectively. At the same time, combined with the Erdoret municipal road project, the changes of CSTR before and after the project in 2016–2020 are studied. Compared the area change of CSTR in 2016–2018 with that in 2018–2020, the annual growth rate before the construction of the municipal road project is about 3.47%. After the completion of the project, the annual growth rate is 7.29%, more than twice the rate before the construction. This method can realize the dynamic monitoring of CSTR, reflect the changes of the residents’ living environment in the region, help analyze the improvement of poverty in Africa, and help understand the changes of African economic conditions.

In today’s rapidly developing remote sensing technology, accurately extracting geographic information from maps is crucial for many key areas such as urban planning, environmental monitoring, and traffic management. However, due to the complexity and variability of remote sensing images, effectively extracting road information from multi-scale geographic images remains a technical challenge. Therefore, the study innovatively proposes a fusion model for panchromatic and multi-spectral images and a fusion map geographic information extraction model from the perspectives of image fusion and road segmentation. Structural similarity and spatial correlation coefficients are crucial for assessing the effectiveness of model image fusion. The experimental results show that in the panchromatic and multispectral remote sensing image datasets, the structural similarity of the model reached 0.023, which was very close to the target value of 0, indicating that the model had excellent image fusion ability. Meanwhile, the spatial correlation coefficient value was also as high as 0.99, close to the target value of 1, further confirming the efficiency of the model in image fusion. Compared with other methods, the designed method had significant advantages in maintaining the continuity of road structure, which could more accurately identify and reproduce the continuity of roads and reduce errors in the extraction process. In summary, the research results are of great significance to improve the accuracy and efficiency of remote sensing image analysis, which not only can provide strong technical support for the application in the above related fields, but also can contribute to the further development and application of remote sensing technology in geographic information extraction.

Coal is one of the important energy sources for industry. When it is mined, it will cause the destruction of bedrock and surface. However, it is more severe in mining shallow-buried multi coal seams (SBMCS). To better reveal the characteristics of the bedrock and surface damage, we have carried out a theoretical analysis, as well as used numerical simulations and field monitoring methods to study the surface and bedrock damage caused by the mining of SBMCS. The characteristics of bedrock and surface failure structure, settlement, and stress distribution were studied and analyzed. The findings show that the collapsed block, formed by the rupture of the overlying stratum, interacts with the surrounding rock to form large cavities and gaps, and the stress concentration occurs between them. The maximum downward vertical concentration stress is about 9.79 MPa. The mining of the lower coal seam can lead to repeated failure of the upper bedrock and goaf. The settlement of bedrock presents gradient change, and the settlement of upper bedrock is large, about 8.0 m, and the maximum settlement is 8.183 m, while that of lower bedrock is small and about 3.5–4.0 m. The weak rock stratum in the bedrock is crushed by the change stress of repeated mining, and formed a broken rock stratum. The cracks in the bedrock develop directly to the ground. On the ground, tensile cracks, compression uplift, stepped cracks, and even collapse pits are easy to cause in mining SBMCS. Affected by repeated mining, the variation of surface vertical stress is complex and disorderly in the middle of the basin, and the variation of horizontal stress is mainly concentrated on the edge of the basin. The maximum stress reaches 100 KPa, and the minimum stress is about 78 KPa. Through theoretical analysis and discussion, the size of the key blocks is directly related to the thickness and strength of the rock stratum.

Soil erosion undermines the sustainable development of land—a vital resource for human survival. Research into the spatiotemporal dynamics of soil erosion is therefore crucial for formulating effective soil and water conservation strategies and advancing ecological protection efforts. In the domain of soil erosion research, the Universal Soil Loss Equation and Revised Universal Soil Loss Equation (USLE/RUSLE) model represent the dominant approach for quantifying soil erosion volumes. While this methodology yields reliable outcomes, it fails to incorporate an assessment of the relative significance of the factors embedded within the model. This study selected the Henan section of the Yellow River Basin as the research area, using monthly remote sensing data from 2010 to 2025 as the main data source. Taking into account factors such as rainfall, slope, elevation, vegetation coverage, and hydrological conservation measures, the RUSLE model was used to calculate and combine Geographic Information System (GIS) geographic detectors for quantitative analysis of soil erosion factors. The results showed the following: (1) The average soil erosion modulus in the study area from 2010 to 2025 was mainly micro and mild erosion. (2) Soil erosion exhibits a certain periodicity, with a year of significant soil erosion occurring every 3–4 years. The overall trend of soil erosion is a decrease. (3) Geographic detector analysis shows that slope has the greatest impact on soil erosion, with larger slopes leading to more severe soil erosion. The influence of each factor ranges from large to small as slope > water conservation measures > rainfall > vegetation coverage > elevation. (4) The interaction between factors can enhance the influence on soil erosion, and the interaction between vegetation cover factors and other factors significantly increases the influence; after interacting with various factors, the slope factor will significantly increase the influence of soil erosion. The research results can provide technical support and decision-making basis for ecological protection in the Yellow River Basin, such as through soil and water conservation, returning farmland to forests, and slope greening; The dominant factors and obvious interaction factors in the research area can provide a scientific basis for subsequent scholars to optimize the parameters of regional models.