Towards Innovative Geospatial Tools for Fit-for-Purpose Land Rights Mapping

Koeva, Mila; Bennett, Rohan; Gerke, M.; Crommelinck, Sophie; Stöcker, Claudia; Crompvoets, Joep; Ho, Serene; Schwering, Angela; Chipofya, Malumbo; Schultz, Carl; Zein, T.; Biraro, Mireille; Alemie, Berhanu Kefale; Wayumba, Robert Nilson; Kundert, Kaspar

doi:10.5194/isprs-archives-xlii-2-w7-37-2017

Cited by 24 publications

(24 citation statements)

References 15 publications

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“…Applications of drone for aerial survey and mapping [49][50][51][52][53][54], forest [55][56][57][58], wildlife conservation [59][60][61], agriculture [22,[27][28][29][30][31], disaster management [90][91][92][93][94], documentation of cultural heritage [95][96][97][98][99][100][101][102], among many others, have been observed over the years. Examples of research cases in each of these domains are summarized in Table 2.…”

Section: Applications Of Drone Technologies In Geospatial Engineeringmentioning

confidence: 99%

A Review on Drone-Based Data Solutions for Cereal Crops

Panday

Pratihast

Aryal

et al. 2020

Drones

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Food security is a longstanding global issue over the last few centuries. Eradicating hunger and all forms of malnutrition by 2030 is still a key challenge. The COVID-19 pandemic has placed additional stress on food production, demand, and supply chain systems; majorly impacting cereal crop producer and importer countries. Short food supply chain based on the production from local farms is less susceptible to travel and export bans and works as a smooth system in the face of these stresses. Local drone-based data solutions can provide an opportunity to address these challenges. This review aims to present a deeper understanding of how the drone-based data solutions can help to combat food insecurity caused due to the pandemic, zoonotic diseases, and other food shocks by enhancing cereal crop productivity of small-scale farming systems in low-income countries. More specifically, the review covers sensing capabilities, promising algorithms, and methods, and added-value of novel machine learning algorithms for local-scale monitoring, biomass and yield estimation, and mapping of them. Finally, we present the opportunities for linking information from citizen science, internet of things (IoT) based on low-cost sensors and drone-based information to satellite data for upscaling crop yield estimation to a larger geographical extent within the Earth Observation umbrella.

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Section: Applications Of Drone Technologies In Geospatial Engineeringmentioning

confidence: 99%

A Review on Drone-Based Data Solutions for Cereal Crops

Panday

Pratihast

Aryal

et al. 2020

Drones

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“…Real-Time Kinematic) differential corrections allowed centimetre level accuracy for reference data. While existing legal boundaries are not accurate enough (with an estimate shift of 1 to 5 m of the 'true' position [59]) to not serve as reference data, they were leveraged on to establish precise boundaries during field survey.…”

Section: Manual Digitisationmentioning

confidence: 99%

Comparing Human Versus Machine-Driven Cadastral Boundary Feature Extraction

et al. 2019

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The objective to fast-track the mapping and registration of large numbers of unrecorded land rights globally has led to the experimental application of Artificial Intelligence in the domain of land administration, and specifically the application of automated visual cognition techniques for cadastral mapping tasks. In this research, we applied and compared the ability of rule-based systems within Object-Based Image Analysis (OBIA), as opposed to human analysis, to extract visible cadastral boundaries from very high-resolution World View-2 images, in both rural and urban settings. From our experiments, machine-based techniques were able to automatically delineate a good proportion of rural parcels with explicit polygons where the correctness of the automatically extracted boundaries was 47.4% against 74.24% for humans and the completeness of 45% for the machine compared to 70.4% for humans. On the contrary, in the urban area, automatic results were counterintuitive: even though urban plots and buildings are clearly marked with visible features such as fences, roads and tacitly perceptible to eyes, automation resulted in geometrically and topologically poorly structured data. Thus, these could neither be geometrically compared with human digitisation, nor actual cadastral data from the field. The results of this study provide an updated snapshot with regards to the performance of contemporary machine-driven feature extraction techniques compared to conventional manual digitising. In our methodology, using an iterative approach of segmentation and classification, we demonstrated how to overcome the weaknesses of having undesirable segments due to intra-parcel and inter-parcel variability, when using segmentation approaches for cadastral feature delineation. We also demonstrated how we can easily implement a geometric comparison framework within the Esri’s ArcGIS software environment and firmly believe the developed methodology can be reproduced.

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“…In response, we are developing innovative, scalable methodologies, using remote sensing data and cadastral intelligence, based on fit-for purpose principles to respond to the continuum of land rights [11,12]. The aim of this paper is to assess user needs (in terms of land administration functions), and how the three remote sensing methodologies under development can meet these needs.…”

Section: Introductionmentioning

confidence: 99%

Innovative Remote Sensing Methodologies for Kenyan Land Tenure Mapping

et al. 2020

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There exists a demand for effective land administration systems that can support the protection of unrecorded land rights, thereby assisting to reduce poverty and support national development—in alignment with target 1.4 of UN Sustainable Development Goals (SDGs). It is estimated that only 30% of the world’s population has documented land rights recorded within a formal land administration system. In response, we developed, adapted, applied, and tested innovative remote sensing methodologies to support land rights mapping, including (1) a unique ontological analysis approach using smart sketch maps (SmartSkeMa); (2) unmanned aerial vehicle application (UAV); and (3) automatic boundary extraction (ABE) techniques, based on the acquired UAV images. To assess the applicability of the remote sensing methodologies several aspects were studied: (1) user needs, (2) the proposed methodologies responses to those needs, and (3) examine broader governance implications related to scaling the suggested approaches. The case location of Kajiado, Kenya is selected. A combination of quantitative and qualitative results resulted from fieldwork and workshops, taking into account both social and technical aspects. The results show that SmartSkeMa was potentially a versatile and community-responsive land data acquisition tool requiring little expertise to be used, UAVs were identified as having a high potential for creating up-to-date base maps able to support the current land administration system, and automatic boundary extraction is an effective method to demarcate physical and visible boundaries compared to traditional methodologies and manual delineation for land tenure mapping activities.

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Towards Innovative Geospatial Tools for Fit-for-Purpose Land Rights Mapping

Cited by 24 publications

References 15 publications

A Review on Drone-Based Data Solutions for Cereal Crops

A Review on Drone-Based Data Solutions for Cereal Crops

Comparing Human Versus Machine-Driven Cadastral Boundary Feature Extraction

Innovative Remote Sensing Methodologies for Kenyan Land Tenure Mapping

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