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Since 2005, Egypt has a new land-use development policy to control unplanned human settlement growth and prevent outlying growth. This study assesses the impact of this policy shift on settlement growth in Assiut Governorate, Egypt, between 1999 and 2020. With symbolic machine learning, we extract built-up areas from Landsat images of 2005, 2010, 2015, and 2020 and a Landscape Expansion Index with a new QGIS plugin tool (Growth Classifier) developed to classify settlement growth types. The base year, 1999, was produced by the national remote sensing agency. After extracting the built-up areas from the Landsat images, eight settlement growth types (infill, expansion, edge-ribbon, linear branch, isolated cluster, proximate cluster, isolated scattered, and proximate scattered) were identified for four periods (1999:2005, 2005:2010, 2010:2015, and 2015:2020). The results show that prior to the policy shift of 2005, the growth rate for 1999–2005 was 11% p.a. In all subsequent periods, the growth rate exceeded the target rate of 1% p.a., though by varying amounts. The observed settlement growth rates were 5% (2005:2010), 7.4% (2010:2015), and 5.3% (2015:2020). Although the settlements in Assiut grew primarily through expansion and infill, with the latter growing in importance during the last two later periods, outlying growth is also evident. Using four class metrics (number of patches, patch density, mean patch area, and largest patch index) for the eight growth types, all types showed a fluctuated trend between all periods, except for expansion, which always tends to increase. To date, the policy to control human settlement expansion and outlying growth has been unsuccessful.

Changes to land use such as the removal of natural vegetation and expansion of urban areas can result in degradation of the landscape and an increase in hydro-meteorological risk. This has led to higher interest by decision-makers and scientists in the future consequences of these drivers. Scenario development can be a useful tool for addressing the high uncertainty regarding modeling future land use changes. Scenarios are not exact forecasts, but images of plausible futures. When studying future land dynamics, emphasis should be given to areas experiencing high rates of socioeconomic change. We have focused on the eastern Italian Alps, which face increasing pressure from tourism development. Identified drivers of local land use change are mostly external and difficult to quantify. This area, characterized by a traditional Alpine landscape, is subject to high levels of hydro-meteorological risk, another reason to study potential future land use changes. We tested a scenario generation method based on existing decisions and assumptions about future tourism development. We aimed to develop a framework leading to plausible scenarios that can overcome data inaccessibility and address external drivers. We combined qualitative methods, such as stakeholder interviews and cognitive mapping, with geospatial methods, such as geographic information systems, geostatistics, and environmental modeling. We involved stakeholders from the beginning to support the steps of generating data, understanding the system of land use change, and developing a land use change model for scenario development. In this way, we generated spatio-temporal scenarios that can assist future spatial planning and improve preparedness for possible undesirable development.