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Based on the perceptions of the local farmers, this study aims to assess the effects of socioeconomic factors and climatic change on the groundwater livelihood system, with a particular focus on in situ Persian wheels/dug wells. Farmers’ perceptions of climate change and how it is affecting their way of life in the Soan River Basin have also been evaluated to determine the most appropriate adaptive interventions. Information and literature about dug wells was unavailable, which stressed the need to carry out this survey. A structured close-ended questionnaire was designed and administered with as much quantitative data as possible. Random sampling opted for a 5 km buffer zone across the Soan River and its tributaries. Union councils having more than 50% of their area lying in the buffer zone were surveyed, and data was collected. Fifty UCs fell within this criterion, and six dug wells from each Union Council were surveyed. The results of our survey collecting local farmer’s perceptions determined that about 70% of respondents agreed about climate change in the Soan Basin of Pakistan, and 62% of farmers reported that climate change severely impacted their livelihood by affecting agricultural productivity and water availability. Ninety-two percent reported summer becoming hot, 72% highlighted that winters are becoming less cold, and 96% reported that average annual rainfall has decreased compared to 10 years before. About 72% of respondents indicated that available water in their dug wells had decreased, and 80% of respondents explained that their crop yield had decreased compared to 10 years before. Sixty percent preferred drip and 35% sprinkler irrigation as efficient water management practices to cope with water shortages. Ninety-five percent of farmers were ready to use solar pumps for irrigation to tame high pumping costs. The study recommends integrating solar pumping with drip and sprinkler irrigation systems to enhance farmers’ cropped area and productivity. T

Large-area crop classification using multi-spectral imagery is a widely studied problem for several decades and is generally addressed using classical Random Forest classifier. Recently, deep convolutional neural networks (DCNN) have been proposed. However, these methods only achieved results comparable with Random Forest. In this work, we present a novel CNN based architecture for large-area crop classification. Our methodology combines both spatio-temporal analysis via 3D CNN as well as temporal analysis via 1D CNN. We evaluated the efficacy of our approach on Yolo and Imperial county benchmark datasets. Our combined strategy outperforms both classical as well as recent DCNN based methods in terms of classification accuracy by 2% while maintaining a minimum number of parameters and the lowest inference time., Comment: Submitted to ICIP 2021

Low socio-economic status has been widely recognized as a significant factor in enhancing a person's vulnerability to climate change including vulnerability to changes in temperature. Yet, little is known about exposure to heat within cities in developing countries, and even less about exposure within informal neighbourhoods in those countries. This paper presents an assessment of exposure to outdoor heat in the South Asian cities Delhi, Dhaka, and Faisalabad. The temporal evolution of exposure to heat is evaluated, as well as intra-urban differences, using meteorological measurements from mobile and stationary devices (April–September 2016). Exposure to heat is compared between low-income and other neighbourhoods in these cities. Results are expressed in terms of air temperature and in terms of the thermal indices Heat Index (HI), Wet Bulb Globe Temperature (WBGT) and Universal Thermal Climate Index (UTCI) at walking level. Conditions classified as dangerous to very dangerous, and likely to impede productivity, are observed almost every day of the measurement period during daytime, even when air temperature drops after the onset of the monsoon. It is recommended to cast heat warnings in terms of thermal indices instead of just temperature. Our results nuance the idea that people living in informal neighbourhoods are consistently more exposed to heat than people living in more prosperous neighbourhoods. During night-time, exposure does tend to be enhanced in densely-built informal neighbourhoods, but not if the low-income neighbourhoods are more open, or if they are embedded in green/blue areas.

Low socio-economic status has been widely recognized as a significant factor in enhancing a person's vulnerability to climate change including vulnerability to changes in temperature. Yet, little is known about exposure to heat within cities in developing countries, and even less about exposure within informal neighbourhoods in those countries. This paper presents an assessment of exposure to outdoor heat in the South Asian cities Delhi, Dhaka, and Faisalabad. The temporal evolution of exposure to heat is evaluated, as well as intra-urban differences, using meteorological measurements from mobile and stationary devices (April–September 2016). Exposure to heat is compared between low-income and other neighbourhoods in these cities. Results are expressed in terms of air temperature and in terms of the thermal indices Heat Index (HI), Wet Bulb Globe Temperature (WBGT) and Universal Thermal Climate Index (UTCI) at walking level. Conditions classified as dangerous to very dangerous, and likely to impede productivity, are observed almost every day of the measurement period during daytime, even when air temperature drops after the onset of the monsoon. It is recommended to cast heat warnings in terms of thermal indices instead of just temperature. Our results nuance the idea that people living in informal neighbourhoods are consistently more exposed to heat than people living in more prosperous neighbourhoods. During night-time, exposure does tend to be enhanced in densely-built informal neighbourhoods, but not if the low-income neighbourhoods are more open, or if they are embedded in green/blue areas.