Application of Remote Sensing Techniques for Change Detection in Land Use/ La...iosrjce
IOSR Journal of Applied Geology and Geophysics (IOSR-JAGG) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of Applied Geology and Geophysics. The journal welcomes publications of high quality papers on theoretical developments and practical applications in Applied Geology and Geophysics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Application of Remote Sensing Techniques for Change Detection in Land Use/ La...iosrjce
IOSR Journal of Applied Geology and Geophysics (IOSR-JAGG) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of Applied Geology and Geophysics. The journal welcomes publications of high quality papers on theoretical developments and practical applications in Applied Geology and Geophysics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Land Use/Land Cover Mapping Of Allahabad City by Using Remote Sensing & GIS IJMER
The present study was carried out to produce and evaluate the land use/land cover maps by on
screen visual interpretation. The studies of land cover of Allahabad city (study area) consist of 87517.47 ha
out of which 5500.35 ha is build up land (Urban / Rural) Area. In this respect, the Build up land (Urban /
Rural) area scorers 6.28% of the total area. It has also been found that about 17155.001ha (19.60 %) of
area is covered by current fallow land. The double/triple crop land of 30178.44ha (34.84%). The area
covered by gullied / ravines is 1539.20 ha (1.75 %) and that of the kharif crop land is 2828.00 ha (3.23 %).
The area covered by other wasteland is 2551.05ha (2.91%). Table 4.1 shows the area distribution of the
various land use and land cover of Allahabad city.
Gene conservation, defined as the policy and management actions taken to assure the continued availability and existence of genetic variation, is an essential component of sustainable forestry.
Land Use/Land Cover Mapping Of Allahabad City by Using Remote Sensing & GIS IJMER
The present study was carried out to produce and evaluate the land use/land cover maps by on
screen visual interpretation. The studies of land cover of Allahabad city (study area) consist of 87517.47 ha
out of which 5500.35 ha is build up land (Urban / Rural) Area. In this respect, the Build up land (Urban /
Rural) area scorers 6.28% of the total area. It has also been found that about 17155.001ha (19.60 %) of
area is covered by current fallow land. The double/triple crop land of 30178.44ha (34.84%). The area
covered by gullied / ravines is 1539.20 ha (1.75 %) and that of the kharif crop land is 2828.00 ha (3.23 %).
The area covered by other wasteland is 2551.05ha (2.91%). Table 4.1 shows the area distribution of the
various land use and land cover of Allahabad city.
Gene conservation, defined as the policy and management actions taken to assure the continued availability and existence of genetic variation, is an essential component of sustainable forestry.
The gradual replacement of one community by another in the development of vegetation towards a climax is the culmination stage in plant succession for a given environment.
Artificial Intelligence is an approach to make a computer, a robot, or a product to think about how smart humans think. AI is a study of how the human brain thinks, learns, decides and work when it tries to solve problems. And finally, this study outputs intelligent software systems. The aim of AI is to improve computer functions that are related to human knowledge, for example, reasoning, learning, and problem-solving.
A disaster is a sudden, calamitous event that seriously disrupts the functioning of a community or society and causes human, material, and economic or environmental losses that exceed the community’s or society’s ability to cope using its own resources. Most disasters are caused by natural termed as natural disasters but sometimes they have human origins and they are called as man-made disasters
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zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
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Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
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ESnet has led the way in helping national facilities—and many other institutions in the research community—configure Science DMZs and troubleshoot network issues to maximize data transfer performance. In this talk we will present a summary of approaches and tips for getting the most out of your network infrastructure using Globus Connect Server.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
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The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
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The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
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Monitoring Land Use and Land Cover through Remote Sensing and GIS
1.
2. CONTENT
1
LAND USE AND LAND COVER
(LULC)
2
LULC TYPES AND THEIR
RESPECTIVE CLASSES
3
LAND USE MAPPING SYSTEM
4
LULC MAPPING APPLICATIONS
5
CONCLUSION
3. LAND USE AND LAND COVER (LULC)
1. LAND USE:
Land use includes a series of operations on land, carried out
by humans, with the intention to obtain products and benefits
through using land resources. It includes agricultural land,
urban area, wildlife management area, recreation area etc.
2. LAND COVER:
Land cover refers to observed physical and biological cover of
earth’s surface. It includes various types of vegetation, grass
land, forest, water bodies, barren land etc. (FAO, 2023).
4. At the local, regional, and national levels, LULC maps are
crucial for planning, management and monitoring programmes.
Provides a better understanding of land utilization
aspects.
It is crucial in the formation of the policies and
programmes essential for development planning.
To prevent the unplanned development of towns and cities
and to promote sustainable urban development.
IMPORTANCE OF LULC MAPS
5. LULC CLASSIFICATION
One of the most widely used applications in remote sensing
is LULC classification. The most frequently used methods
are:
(Source: Paris et al., (2019), eo4society.esa.int)
Supervised
classification
Unsupervised
classification
6. Unsupervised classification and Supervised classification
Classification Advantages Disadvantages
Unsupervised
classification
No prior knowledge of the
region is required.
Allows for minimization of the
human error.
Spectrally distinct areas
presented which may not have
been obvious to the human eye.
Spectral grouping may not
correspond to information classes
of interest to the analyst.
Analyst has little control over the
classes.
Supervised
classification
Analyst have control.
Operator can often detect and
rectify images.
Collecting training data is time
consuming and costly.
There is no way to recognize and
represent categories which are not
represented in the training data.
(Source: slideshare.net, Dhanendra Bahekar)
7. Normalized Difference Vegetation Index (NDVI)
(Source: www.satpalda.com, 2018)
The NDVI is calculated using near-infrared (NIR) and visible
red (R) light to look for a single band normalized vegetation
index in plants. Then, the NDVI is calculated using a digital
number (DN) and several band values (Özyavuz et al., 2015).
For Landsat 8, the NDVI is derived using the NIR (Band 5),
and the Red (Band 4) band.
NDVI = (NIR – Red) / (NIR + Red)
or
NDVI = (Band 5 - Band 4) / (Band 5 + Band 4)
8. LULC METHODOLOGY
DATA
ACQUISITION
DIFFERENT TIME
PERIODS DATA
IMAGE PRE-
PROCESSING
Geometric correction
Radiometric correction
Image enhancement
IMAGE CLASSIFICATION
Unsupervised classification
Supervised classification
LULC MAP
ACCURACY ASSESMENT
GPS & Google
Earth data
NDVI analysis
LULC CHANGE
DETECTION
LULC: Land use and land cover
GPS: Global Positioning System
NDVI: Normalized Difference
Vegetation Index
9. LULC TYPES AND THEIR RESPECTIVE CLASSES
TYPES CLASSES
Residential
Commercial and Services
Industrial
Communications and Utilities
Mixed Urban or Built-up Land
Other Urban or Built-up Land
Cropland and Pasture
Orchards, Nurseries, and Ornamental
Horticultural areas
Confined Feeding operations
Herbaceous Rangeland
Shrub and Brush Rangeland
Mixed Rangeland
Deciduous Forest Land
Evergreen Forest Land
Mixed Forest Land
(Source: www.satpalda.com, 2018)
10. LULC TYPES AND THEIR RESPECTIVE CLASSES (cont.)
TYPES CLASSES
Rivers
Streams and Canals
Lakes
Reservoirs
Bays and Estuaries
Forested Wetland
No forested Wetland
Dry Salt Flats
Beaches
Sandy Areas other than Beaches
Bare Exposed Rock
Strip Mines, Quarries, and Gravel Pits
Transitional Areas
Mixed Barren Land
Perennial Snowfields
Glaciers
(Source: www.satpalda.com, 2018)
11. Figure 1: Land use and land cover map of India (Roy et al., 2015)
12. LAND USE MAPPING SYSTEM
LEVEL SCALE DATA SOURCE FREQUENCY
1.National 1:500,000
Medium Resolution
(56 m) Satellite data
Annually
2. State 1:250,000
Medium Resolution
(24 m) Satellite data
Once in 5 years
3. District 1:50,000
Medium Resolution
(24 m) Satellite data
Once in 5 years
4. Village 1:10,000
High resolution
satellite data (2.5 m)
Once in 8 years
5. Cadastral 1:5,000
Very High resolution
satellite data (<1 m)
Once in 3 years
14. LULC MAPPING APPLICATIONS
Baseline mapping for GIS input
Urban expansion / encroachment
Natural resource management
Wildlife habitat protection
Routing and logistics planning for
exploration/resource extraction activities
Identification of roads, clearings, bridges,
land/water interface
Damage delineation (tornadoes, flooding,
volcanic, fire)
Legal boundaries for tax and property
evaluation
15. ⁂ Maps of an area’s land use and land cover (LULC) give
users a better understanding about the current landscape.
The annual monitoring of temporal dynamics of agricultural
ecosystems, forest conversions, surface water bodies, etc.
will be made possible by LULC information on national
spatial databases.
⁂ LULC maps play a significant and prime role in planning,
management and monitoring programmes at local, regional
and national levels.
⁂ For ensuring sustainable development, it is necessary to
monitor the on going process on land use/land cover pattern
over a period of time.
CONCLUSION
16. REFERENCES
1. https://www.satpalda.com/blogs/significance-of-land-use-land-cover-
lulc-maps
2. https://www.slideshare.net/DhanendraBahekar/land-cover-and-land-use
3. Roy, P.S., P. Meiyappan, P.K. Joshi, M.P. Kale, V.K. Srivastav, S.K.
Srivasatava, M.D. Behera, A. Roy, Y. Sharma, R.M.
Ramachandran, P. Bhavani, A.K. Jain, and Y.V.N. Krishnamurthy.
(2016). Decadal Land Use and Land Cover Classifications across India,
1985, 1995, 2005. ORNL DAAC, Oak Ridge, Tennessee, USA.
https://doi.org/10.3334/ORNLDAAC/1336
4. Özyavuz, Murat & Bilgili, Cemil & Salıcı, Aylin. (2015).
Determination of vegetation changes with NDVI method. Journal of
environmental protection and ecology. 16: 264-273.
5. Paris, Claudia & Bruzzone, Lorenzo & Fernandez-Prieto, Diego.
(2019). A Novel Approach to the Unsupervised Update of Land-Cover
Maps by Classification of Time Series of Multispectral Images. IEEE
Transactions on Geoscience and Remote Sensing. PP. 1-19.
10.1109/TGRS.2018.2890404.