In this study, we evaluated the effectiveness of photochemical reflectance index (PRI) and non-photochemical quenching (NPQ) for assessing water stress in maize for the purpose of developing remote sensing techniques for monitoring water deficits in crops. Leaf-level chlorophyll fluorescence and canopy-level PRI were measured concurrently over a maize field with five different irrigation treatments, ranging from 20% to 90% of the field capacity (FC). Significant correlations were found between leaf-level NPQ (NPQ_leaf) and the ratio of chlorophyll to carotenoid content (Chl/Car) (R² = 0.71, p < 0.01) and between NPQ_leaf and the actual photochemical efficiency of photosystem II (ΔF/F_m′) (R² = 0.81, p < 0.005). At the early growing stage, both canopy-level PRI and NPQ_leaf are good indicators of water stress (R² = 0.65 and p < 0.05; R² = 0.63 and p < 0.05, respectively). For assessment of extreme water stress on plant growth, a relationship is also established between the quantum yield of photochemistry in PSII (ΦP) and the quantum yield of fluorescence (ΦF) as determined from photochemical quenching (PQ) and non-photochemical quenching (NPQ_leaf) of excitation energy at different water stress levels. These results would be helpful in monitoring soil water stress on crops at large scales using remote sensing techniques.

The spatial–temporal assessment of vector diseases is imperative to design effective action plans and establish preventive strategies. Therefore, such assessments have potential public health planning-related implications. In this context, we here propose an integrated spatial disease evaluation (I-SpaDE) framework. The I-SpaDE integrates various techniques such as the Kernel Density Estimation, the Optimized Hot Spot Analysis, space–time assessment and prediction, and the Geographically Weighted Regression (GWR). It makes it possible to systematically assess the disease concentrations, patterns/trends, clustering, prediction dynamics, and spatially varying relationships between disease and different associated factors. To demonstrate the applicability and effectiveness of the I-SpaDE, we apply it in the second largest city of Pakistan, namely Lahore, using Dengue Fever (DF) during 2007–2016 as an example vector disease. The most significant clustering is evident during the years 2007–2008, 2010–2011, 2013, and 2016. Mostly, the clusters are found within the city’s central functional area. The prediction analysis shows an inclination of DF distribution from less to more urbanized areas. The results from the GWR show that among various socio-ecological factors, the temperature is the most significantly associated with the DF followed by vegetation and built-up area. While the results are important to understand the DF situation in the study area and have useful implications for public health planning, the proposed framework is flexible, replicable, and robust to be utilized in other similar regions, particularly in developing countries in the tropics and sub-tropics.

This study employed big data and spatial analysis to assess property values in two cities, Lahore and Faisalabad. Traditional housing price models overlook spatial nuances, focusing solely on structural attributes. To address this, we constructed valuation models using ordinary least square regression and Fast Geographic Weighted Regression (FastGWR), implemented through Python and MPI, based on spatial variables. The models explained up to 75 percent of variance in Faisalabad and around 85 percent in Lahore. Factors like floor area, proximity of health facilities, recreational sites, and marketplaces add a premium to prices, while the nearness of educational institutions, worship places, and solid waste transfer stations or dumping sites lessen the property values in both cities. However, the proximity of industrial units and graveyards affects property values negatively in Lahore but positively in Faisalabad. This study highlights the critical significance of spatial factors in urban immovable property appraisal. As a result, it is recommended to integrate these factors into the process of policy formulation and urban planning.1. INTRODUCTIONPakistan’s real estate market emerges as a potential economic powerhouse, with a substantial portion of the nation’s wealth concentrated within its real estate assets, estimated at 60-70 percent. While the sector contributes around 2 percent to the GDP, the combined impact of housing and construction reaches nearly 9 percent. The value of Pakistan’s real estate sector, evaluated at approximately $700 billion by the Federal Board of Revenue, signifies its economic significance. Impressively, returns on investment can soar beyond 100 percent (Ouattara et al., 2018). However, this promising market is juxtaposed against a backdrop of challenges and disparities. Pakistan’s population, surpassing 225 million, expands annually at a 2.4 percent rate, characterised by an average household size of 6.5 (Pakistan Bureau of Statistics, 2019). With a yearly housing requirement of 700,000 units, merely half of this demand is met, leading to an alarming gap of roughly 10 million units (Rizvi, 2018). This housing shortage necessitates innovative strategies, particularly in the realm of low-cost housing schemes. The intricacies of property valuation further complicate the real estate landscape. Government land acquisitions relying on DC valuation tables often incite public protests due to perceived undervaluation (Sabir et al., 2017). The importance of precise valuation for equitable compensation is underscored by research (Malaitham et al., 2020). Notably, property valuation is not just pivotal for buyers and sellers; it resonates with stakeholders such as investors, banks, agents, and insurers. The geographical location holds substantial influence over a property’s price, further emphasising the necessity for accurate valuation (Mankad, 2021).In dynamically growing cities, the accurate prediction of urban land use evolution plays a pivotal role in fostering sustainable urban planning (Liang et al., 2018) such as Lahore and Faisalabad in Pakistan. Notably, vast untapped potential resides within public properties, including Government Officers Residences (GORs) and railway lands, representing latent avenues for wealth creation. Leveraging these assets effectively can substantially contribute to economic prosperity. The implications of this study extend to policymakers, offering insights to navigate the intricate domains of housing and urban development. A robust housing market stands as a linchpin of a resilient economy; however, Pakistan’s housing sector faces an intricate array of challenges. Urbanisation and migration galvanise demand within urban centers, an issue compounded by insufficient supply catalysed by diverse factors. Shortcomings in land usage, planning, and property rights impede progress, while inadequate revenue collection from property taxes curtails infrastructure financing. The labyrinthine regulations further stall land development, exacerbating housing availability discrepancies, particularly pronounced in megacities like Karachi and Lahore. Notably, housing construction trails behind the meteoric pace of population expansion. Skyrocketing market conditions render housing unattainable for many, channeling them towards informal settlements (Dowall & Ellis, 2009; Haque, 2015; Wani et al., 2020; Yuen & Choi, 2012). The challenges are particularly pronounced within Punjab’s housing markets, accentuated in cities like Lahore and Faisalabad, grappling with deficits in affordable housing (Malik et al., 2020; Wajahat, 2012). A glaring obstacle lies in the hands of speculative investors who control 75 percent of residential plots, perpetuating this complex issue (Zaman & Baloch, 2011). This practice thrives on secure real estate investments and tax loopholes, compounding the predicament. Although plot prices surge significantly (Gul et al., 2018), official valuations lag behind, generating volatility in Pakistan’s property prices. The repercussions of such fluctuations extend to the public, shouldering the burden of investor gains. Despite intermittent housing policies, the issue remains inadequately addressed, emphasising the urgency of public sector interventions to stabilise spiraling prices (Ahmed et al., 2021).

Deep-learning research on flood inundation mapping is driven largely by architectural innovation and single-run reporting. We ask whether the segmentation architecture or the input representation contributes more to accuracy in flood-event surface-water mapping. We introduce GeoHybridNet, a hybrid CNN–Transformer network, and compare it on the hand-labeled Sen1Floods11 benchmark with a U-Net, an Attention U-Net, a parameter-matched wide U-Net, and SegFormer under matched data and training. The four convolutional and hybrid models differ by at most 1.4 IoU points, and none are significantly different under event-level tests; a compact SegFormer trained from scratch is about 3.5 points lower. In factorial designs crossing three architectures with up to five input representations (five seeds each), input representation explains about 95% of the variance in water IoU and architecture about 1%. The dominant input factor is radiometric normalization: raw bands standardized per chip reach only 60% IoU and adding NDWI restores 76%, whereas the same bands standardized with training-set statistics reach 77.5% without any index. The pattern holds when permanent water is excluded. On three held-out flood events, accuracy drops by 24–26 IoU points for every model. On this benchmark, input preparation and honest evaluation matter more than architecture.

Road traffic accidents (RTAs) are a leading cause of global mortality, ranking 12th overall and 1st for ages 5–29 years, with an estimated 1.19 million annual deaths (World Health Organization 2023...