Automated Extraction of the Archaeological Tops of  Qanat Shafts from VHR Imagery in Google Earth

Luo, Lei; Wang, Xinyuan; Guo, Huadong; Liu, Chuansheng; Liu, Jie; Li, Li; Du, Xiaocui; Qian, Guoquan

doi:10.3390/rs61211956

Cited by 42 publications

(38 citation statements)

References 55 publications

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“…Mapping individual karezes and their recharge zones is easily accomplished using freely available digital maps like Google Earth ( Fig. 3; Luo et al 2014), digital elevation maps and geological maps (USGS 2010; partly shown in Fig. 2).…”

Section: Strategies For Rehabilitating Karezesmentioning

confidence: 99%

Karez (qanat) irrigation in the Helmand River Basin, Afghanistan: a vanishing indigenous legacy

Goes

Parajuli²,

Haq

et al. 2016

Hydrogeol J

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A karez is a gently sloping tunnel into a hillside with a series of vertical shafts. At the upstream end, the karez depresses the water table such that groundwater enters the tunnel. Farmers all over Afghanistan have built and managed karezes for centuries using indigenous knowledge. This report focuses on karezes in the Helmand River Basin in southern Afghanistan, and describes the location of karezes in relation to geology, technological and managerial aspects of karez irrigation, and their current status. Karez irrigation has declined in recent decades due to the following: a prolonged reduction in precipitation, increase in number of boreholes that lower the water table below the karez tunnel, breakdown in communitybased management, and reduced maintenance. Systematic field measurements are a challenge in the Helmand Basin due to security constraints. The current condition and management of the karezes have been assessed through short field visits and structured focus-group discussions with karez farmers and staff from provincial departments. The surveys indicate that over half of the karezes in the Helmand Basin have gone dry. Furthermore, the flow in karezes that are still operational has also declined significantly. The report demonstrates the value of using data from the US National Centres for Environmental Prediction (NCEP) Reanalysis 1 project, to estimate historic precipitation for various karez zones in this data-poor basin. Strategies for rehabilitating karezes are discussed. Rehabilitation is financially expensive in comparison to drilling new boreholes, but karezes are part of the national heritage of Afghanistan and can facilitate social cohesion.

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Section: Strategies For Rehabilitating Karezesmentioning

confidence: 99%

Karez (qanat) irrigation in the Helmand River Basin, Afghanistan: a vanishing indigenous legacy

Goes

Parajuli²,

Haq

et al. 2016

Hydrogeol J

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“…; Trathan ; similarly, archeological remote sensing: Luo et al. ). In contrast, the surface characteristics of the HAI are not homogenous and many items in the background appear similar to nests in the aerial imagery, including drab vegetation and senescent plant matter.…”

Section: Introductionmentioning

confidence: 97%

Semi‐automated detection of eagle nests: an application of very high‐resolution image data and advanced image analyses to wildlife surveys

Andrew

Shephard

2017

Remote Sens Ecol Conserv

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Very high-resolution (VHR) image data, including from unmanned aerial vehicle (UAV) platforms, are increasingly acquired for wildlife surveys. Animals or structures they build (e.g. nests) can be photointerpreted from these images, however, automated detection is required for more efficient surveys. We developed semi-automated analyses to map white-bellied sea eagle (Haliaeetus leucogaster) nests in VHR aerial photographs of the Houtman Abrolhos Islands, Western Australia, an important breeding site for many seabird species. Nest detection is complicated by high environmental heterogeneity at the scale of nests (~1-2 m), the presence of many features that resemble nests and the variability of nest size, shape and context. Finally, the rarity of nests limits the availability of training data. These challenges are not unique to wildlife surveys and we show how they can be overcome by an innovative integration of object-based image analyses (OBIA) and the powerful machine learning one-class classifier Maxent. Maxent classifications using features characterizing object texture, geometry and neighborhood, along with limited object color information, successfully identified over 90% of high quality nests (most weathered and unusually shaped nests were also detected, but at a slightly lower rate) and labeled <2% of objects as candidate nests. Although this overestimates the occurrence of nests, the results can be visually screened to rule out all but the most likely nests in a process that is simpler and more efficient than manual photointerpretation of the full image. Our study shows that semi-automated image analyses for wildlife surveys are achievable. Furthermore, the developed strategies have broad relevance to image processing applications that seek to detect rare features differing only subtly from a heterogeneous background, including remote sensing of archeological remains. We also highlight solutions to maximize the use of imperfect or uncalibrated image data, such as some UAV-based imagery and the growing body of VHR imagery available in Google Earth and other virtual globes.

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“…Google Earth images have only three bands: red, green, and blue (RGB). Owing to the poor spectral information, there have not been too many ISA or land-cover mapping studies based on Google Earth image, as they are mainly used for small object detection, such as village buildings [18], scattered shrubs [19], ships [20], and qanat shafts [21]. As a free high spatial resolution image data source, Google Earth has a high potential for high resolution ISA mapping.…”

Section: Introductionmentioning

confidence: 99%

DMBLC: An Indirect Urban Impervious Surface Area Extraction Approach by Detecting and Masking Background Land Cover on Google Earth Image

Huang

Chen

et al. 2018

Remote Sensing

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Implying the prosperity and development of the city, impervious surface area (ISA) is playing an increasingly important role in ecological processes, microclimate, material and energy flows, and urban flood. The free sub-meter resolution Google Earth image, which is integrated by several high spatial resolution data, appears to have potential for high-resolution ISA extraction, where present study is rare and performances remain to be improved. Due to the high spatial and spectral variation of the urban environment as well as confusion between ISA and soil, the accurate delineating of ISA with traditional (direct) methods can be costly and time-consuming, which is in a word resource-intensive. However, this paper presents a novel indirect ISA extraction conceptual model and a new detecting and masking background land cover (DMBLC) approach that: uses a freely available, high-resolution dataset; requires a reduced set of training samples; and consists of relatively simple, common, and feasible image processing steps. The key characteristic of DMBLC is to detect the background of ISA (vegetation, soil, and water) accurately and obtain the ISA by masking the background. The approach relies on background detection to avoid the predicaments of direct ISA extraction. Water can be directly gained by water body vector data, in DMBLC; mixture tuned matched filtering (MTMF) is exploited to detect vegetation and soil, image segmentation is used to mitigate the spectral variation problem within the same land cover, and segment rectangularity reduces the confusion between ISA and soil. From experiments in a core area of Fuzhou, China, the DMBLC approach reached high performance and outperformed the powerful traditional support vector machines (SVM) method (overall accuracy of 94.45% and Kappa coefficient of 0.8885, compared to 86.44% and 0.7329, respectively). From the comparison of different levels of complexity within the inner processing steps, it is confirmed that the DMBLC approach is a powerful and flexible changed framework for indirect ISA extraction, which can be improved by using more advanced inner methods.

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Automated Extraction of the Archaeological Tops of Qanat Shafts from VHR Imagery in Google Earth

Cited by 42 publications

References 55 publications

Karez (qanat) irrigation in the Helmand River Basin, Afghanistan: a vanishing indigenous legacy

Karez (qanat) irrigation in the Helmand River Basin, Afghanistan: a vanishing indigenous legacy

Semi‐automated detection of eagle nests: an application of very high‐resolution image data and advanced image analyses to wildlife surveys

DMBLC: An Indirect Urban Impervious Surface Area Extraction Approach by Detecting and Masking Background Land Cover on Google Earth Image

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