KR20220009272A - Method for measuring carbon emission using roof image of aerial picture and system for measuring carbon emission - Google Patents

Abstract

The present invention provides a method for calculating carbon emission using a roof image of a drone image. In an embodiment, the method for calculating carbon emission using a roof image of a drone image includes the steps of: acquiring a drone image including a roof image according to a visible ray of each of a plurality of buildings through drone photography; extracting a light reflectance value (LRV) defined for each color from the color constituting each of the roof images; calculating energy consumption of each of the plurality of buildings from the LRV; and calculating carbon emission from the calculated energy consumption.

Description

Calculation method and carbon emission measurement system using roof image of drone image

The present invention relates to a method of calculating carbon emissions using a roof image through an image taken using a drone and a carbon emission measuring system.

The Kyoto Protocol introduced the Emissions Trading system for greenhouse gas to induce flexible implementation while compulsorily imposing obligations on developed countries to reduce greenhouse gas emissions.

The emission trading system determines the total amount of global greenhouse gas emissions and allocates them to each country in order to reduce greenhouse gas emissions such as carbon dioxide and methane. In other words, if a country emits greenhouse gases less than the quota granted to it, it can sell the surplus to other countries for money. to buy from other countries. On the other hand, carbon dioxide (CO2) emits the largest amount of greenhouse gases, so emission credits are usually called 'carbon credits'.

For these reasons, the government and public institutions are making various efforts to reduce carbon emissions, and when planning regional development models such as city, road, and regional development, predict carbon emission and reflect it in the regional development model to ensure the environment Emission-reducing urban or regional spaces are being modeled. Nevertheless, due to the problem that the method of estimating and calculating the amount of carbon is not economically and effective, the measurement of carbon emission in cities has not been performed.

An object of the present invention is to provide a carbon emission calculation method and a carbon emission measurement system that can predict carbon emission generated in a building and energy consumed for heating and cooling in a building in an inexpensive and fast manner.

An object of the present invention is to provide a carbon emission calculation method and a carbon emission measurement system that can periodically observe and predict carbon emission generated in a building and energy consumed for heating and cooling in a building.

The object of the present invention is not limited thereto, and other objects not mentioned will be clearly understood by those skilled in the art from the following description.

The present invention provides a method of calculating carbon emissions using a roof image of a drone image. In one embodiment, the method of calculating carbon emissions includes: acquiring a drone image including a roof image according to each visible light of a plurality of buildings by drone photography; extracting a light reflectance value (LRV) defined for each color from the color constituting the respective roof image; calculating energy consumption of each of the plurality of buildings from the LRV; and calculating carbon emission from the calculated consumed energy.

In an embodiment, the higher the value of the LRV, the lower the energy consumption may be calculated.

In an embodiment, the drone image may be captured for each administrative area, and the carbon emission may be calculated according to the administrative area.

In an embodiment, the acquiring the drone image includes acquiring a plurality of drone images according to a plurality of different times for the same roof, and forming the roof image from the plurality of drone images on the same roof Further comprising the step of determining the material of the roof from the pixel value change and the time information at which the drone image was taken, in the step of calculating the energy consumption of each of the plurality of buildings, at least one of the reflectance or the emissivity of the material of the roof and The energy consumption may be calculated from the LRV.

In an embodiment, data on a building in which energy consumption is detected in a predetermined range or more among the plurality of buildings in the drone image may be notified to the outside.

In addition, the present invention provides a carbon emission measurement system. In an embodiment, the carbon emission measurement system includes: a data receiver for acquiring drone image data including a roof image according to each visible light of a plurality of buildings by drone photography; a roof determination unit for determining the roofs of the plurality of buildings from the drone image; an LRV extraction unit for extracting a light reflectance value (LRV) defined for each color from each color constituting the roof of the plurality of buildings; an energy consumption calculation unit calculating energy consumption of each of the plurality of buildings from the LRV; and a carbon emission calculation unit for calculating carbon emission from the calculated energy consumption.

In an embodiment, the energy calculator may calculate that the energy consumption is lower as the value of LRV is higher.

In one embodiment, the drone image is composed of a plurality, and includes an integrator for integrating the plurality of drone images, the integrator integrates the plurality of drone images according to administrative regions, and the carbon emission calculator is the It is possible to calculate the carbon emission according to the administrative district.

In one embodiment, the data receiving unit receives a plurality of drone images according to a plurality of different times for the same roof, and the roof determining unit, pixels constituting the roof image in the plurality of drone images for the same roof The material of the roof is determined from the value change and the time information at which the drone image was taken, and the energy consumption calculating unit may calculate the energy consumption from at least one of reflectance or emissivity according to the material of the roof and the LRV.

In one embodiment, the drone image may further include a notification unit informing the outside of data on a building in which the energy consumption of the plurality of buildings is detected to be greater than or equal to a predetermined range.

According to an embodiment of the present invention, it is possible to estimate carbon emissions generated in a building and energy consumed for heating and cooling in a building in an inexpensive and quick way.

According to an embodiment of the present invention, it is possible to periodically observe and predict the carbon emission generated in the building and the energy consumed for heating and cooling in the building.

Effects of the present invention are not limited to the above-described effects, and effects not mentioned will be clearly understood by those of ordinary skill in the art from the present specification and accompanying drawings.

1 is a block diagram of a carbon emission measurement system according to an embodiment of the present invention.
2 is a flowchart illustrating a method of calculating carbon emission according to an embodiment of the present invention.
3 is a color space according to the CIELAB color system defined by the International Lighting Commission.
4 is a graph of spectral irradiance according to a wavelength of a light beam.
5 is a drone image taken according to an embodiment of the present invention.
FIG. 6 is an extract of 8 samples from the roof image included in the drone image of FIG. 5 .
7 shows eight roof image samples shown in FIG. 6 in drone images taken with a thermal imaging camera.
8 is a graph showing eight samples of the roof image of FIG. 6 on the LRV (Light Reflectance Value) and surface temperature (Surface temperature) axes.
9 is an image illustrating administrative districts according to an embodiment.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those of ordinary skill in the art can easily implement them. However, the present invention may be embodied in several different forms and is not limited to the embodiments described herein. And in order to clearly explain the present invention in the drawings, parts irrelevant to the description are omitted, and similar reference numerals are attached to similar parts throughout the specification.

The terms used in the present application are only used to describe specific embodiments, and are not intended to limit the present invention. The terms used in the present application have been selected as currently widely used general terms as possible while considering the functions in the present invention, but these may vary depending on the intention, precedent, or emergence of new technology of a person skilled in the art. In addition, in a specific case, there is a term arbitrarily selected by the applicant, and in this case, the meaning will be described in detail in the description of the corresponding invention. Therefore, the term used in the present invention should be defined based on the meaning of the term and the overall content of the present invention, rather than the name of a simple term.

Throughout the specification, when a part is "connected" with another part, this includes not only the case of being "directly connected" but also the case of being "electrically connected" with another element interposed therebetween. . In addition, when a part "includes" a certain component, this means that other components may be further included rather than excluding other components unless otherwise stated. In other words, the singular expression includes the plural expression unless the context clearly dictates otherwise. In the present application, terms such as “comprise” or “have” are intended to designate that a feature, number, step, operation, component, part, or combination thereof described in the specification exists, but one or more other features It should be understood that this does not preclude the existence or addition of numbers, steps, operations, components, parts, or combinations thereof.

1 is a block diagram of a carbon emission measurement system according to an embodiment of the present invention. Referring to FIG. 1 , the carbon emission measurement system 100 according to an embodiment includes a data receiving unit 110 , a data integrating unit 120 , a roof determining unit 130 , an LRV extracting unit 140 , and energy consumption calculation. It includes a unit 150 , a carbon emission calculation unit 160 , and a notification unit 170 . In the carbon emission measurement system 100 according to embodiments of the present invention, the data receiver 110 , the data integrator 120 , the roof determiner 130 , the LRV extractor 140 , the energy consumption calculator 150 , Of course, the configuration, storage and management of the carbon emission calculation unit 160 can be realized in the form of hardware, software, or a combination of hardware and software. Such file data and/or the software may be stored in a volatile or non-volatile storage device such as, for example, read only memory (ROM), or, for example, random access memory (RAM), whether erasable or rewritable, memory, such as a memory chip, device or integrated circuit, or optically or magnetically writable, such as, for example, a Compact Disk (CD), a Digital Versatile Disc (DVD), a magnetic disk or a magnetic tape, and a mechanical (eg, Of course, it may be stored in a storage medium readable by a computer).

The data receiver 110 acquires drone image data. The drone image data is image data obtained from drone photography. Drone image data may be acquired from a camera attached to an unmanned aerial vehicle (UAV). The drone image according to an embodiment of the present invention uses a camera that captures a visible light region. The camera for photographing the visible light region may be a digital camera that has been generally distributed to the public. The drone image data includes roof images of a plurality of buildings. In one embodiment, unmaned aerial vehicles (Unmanned Aerial Vehicles) can fly at a low altitude in a very densely populated area through a legal procedure in Korea. image can be obtained. Since images taken at low altitude are less affected by weather such as clouds and dust, LRV can be acquired with high accuracy.

The data integrator 120 integrates drone image data. Drone image data is composed of a plurality of data as it is captured by a camera. The data integrator 120 may extract one drone image data by integrating a plurality of drone image data. As an example, the data integrator 120 may integrate a plurality of drone images for each administrative district, as will be described with reference to FIG. 6 to be described later. Also, the data integrator 120 may integrate and compare drone images captured at a plurality of different times in one place.

The roof determination unit 130 determines each roof of a plurality of buildings from the drone image. The roof determination unit 130 determines each roof of a plurality of buildings from each drone image. Also, the roof determination unit 130 may determine the material of the roof based on time information and changes in pixel values of drone images captured at different times for one roof.

The LRV extraction unit 140 extracts a light reflectance value (LRV) defined for each color from each color constituting the roof extracted by the roof determination unit 130 .

The energy consumption calculation unit 150 calculates the energy consumption of each building from the LRV obtained from the color extracted for each roof. In the case of flat roofs, which occupy a large proportion of the roof types of Korean buildings, most of the roof layers are composed of cement. In addition, the surface of the flat roof is constructed with urethane paint produced for the purpose of waterproofing, and it is confirmed that it is distributed in various colors such as green, gray, and blue. Therefore, in order to evaluate the cool roof performance of Korean buildings that have already been constructed, the justification for analyzing the color of the roof surface as the main variable is derived from among various variables. Similar to the principle that a white shirt lowers the body's heat load, the color of the roof is one of the important variables in evaluating the cool roof performance, and an objective evaluation index is required. LRV (Light Reflectance Value) is evaluated as a tool for quantitatively measuring the amount of visible light reflection in a specific area based on pixel values. Since energy consumption to maintain an appropriate indoor temperature depends on the LRV on the roof of a building, the LRV can be used as the most direct and key reflectivity metric to evaluate the cooling and heating load caused by the cool roof. The LRV can be acquired with drone images without direct contact with the roof, and can be probed for local-level targets in a short time. Therefore, LRV can be used as a key indicator to clearly quantify solar reflectance and to indicate various variables related at each stage of the cool roof evaluation. The surface temperature corresponds to the most direct evaluation index that reflects the color and characteristics of the roof surface material. The energy consumption calculation unit 150 calculates that the higher the value of LRV, the lower the energy consumption.

The carbon emission calculation unit 160 calculates carbon emission from the energy consumption calculated by the energy consumption calculation unit 150 . The greater the energy consumption calculated by the energy consumption calculation unit 150 is, the greater the carbon emission is calculated.

2 is a flowchart illustrating a method of calculating carbon emission according to an embodiment of the present invention. A carbon emission calculation method will be described with reference to FIG. 2 . According to an embodiment of the present invention, it is possible to estimate the energy consumption of the building from the image in the visible light region.

To calculate carbon emission, a drone image is acquired (S10). The obtained drone image may be received by the data receiver 110 to the system 100 . Drone images are acquired using an aircraft. The photo acquired through the aircraft may communicate in real time with the data receiver 110 through a wireless communication module installed in the aircraft. One example of an aircraft is an unmanned aerial vehicle (UAV). Images acquired from UAVs can provide high spatial detail and are not constrained by the orbital time of satellites or flight plans of manned aircraft. Recently, various manufacturers have developed and developed a function that enables direct acquisition of high-resolution aerial images, and the trend is to mount it on the aircraft and release it. UAV-related technologies are developing at a rapid pace, and it is expected that new developments will be able to provide a larger amount of high-resolution images at a lower price for the next 10 years. High-resolution images are expected to lead to high-level applied analysis by acquiring new influence variables through high-level detail visualization in macroscopic cool-loop monitoring. The drone image taken through an embodiment of the present invention corresponds to a visible spectrum as a region corresponding to a spectral spectrum of 0.4 μm to 0.7 μm, which is a range that can be read by the human eye.

The roof of the building is determined from the obtained drone image (S20). The drone image includes roof images of a plurality of buildings. A plurality of building roofs are determined as respective roofs by the roof determining unit 130 .

A color constituting the roof is extracted from the determined roof, and an LRV corresponding to the color is extracted (S30). Then, the energy consumption of the building is calculated from the extracted LRV (S40). The color of the roof depends on the cool roof concept of the architecture. Cool roof is a technique to reduce indoor heat inflow by reflecting and radiating solar radiation energy (ultraviolet, visible, infrared) absorbed by the roof surface of a building into the atmosphere using materials with high reflectivity (coating material, film material, tile, etc.) to be. It is recognized as an eco-friendly technology that increases cooling efficiency by reducing heat absorption through rapid dissipation of solar radiation from the roof surface and reduced heat gain. Since the roof of a building is a passageway that directly contacts the solar radiation, the use of a color paint with high solar energy reflectance on the roof improves the thermal environment of the atmosphere inside and around the building. When a cool roof is installed, about 30% of the energy consumed in the building is reduced (Reference Papers: Sergio Boixo et al. 2011; Akbari and Konopacki 2004; Simpson and McPherson 1997; Belusi and Barozi 2015).

The representative independent variables affecting the cool roof performance are recognized as reflectance and emissivity. The surface temperature is a dependent variable by the cool roof performance factor, and has a useful value as an index that can verify the actual cool roof performance. Since the surface temperature eventually affects the indoor temperature (energy consumption of a building), measuring the surface temperature is a key factor in evaluating the cooling roof performance. As the surface temperature of the roof is high and low, the air conditioner is operated to keep the indoor temperature constant, resulting in energy consumption. In this regard, the surface temperature of the roof is a direct variable that evaluates the cooling load of a building. Assessing the thermal load using the surface temperature of the roof can be considered as an objective measure to validate the effectiveness of the cool roof installation. LRV (Light Reflectance Value) can be extracted using CIELAB (CIEL*a*b*), which is a color system regulated by the International Commission on Illumination (CIE). The basic concept of color is explained by three criteria: hue, brightness or luminance, and intensity. The lightness of a color is quantified as a relative lightness value as a percentage from dark to maximum light (difuse white). For example, the difference between light blue and dark blue can be interpreted as a difference in brightness. The CIELAB color system was designed to represent a uniform color space perceived by humans, rather than the RGB (Red, Gren, Blue) color system that outputs physical devices such as computer displays and printers. CIELAB has been internationally recognized as a color system for quantifying the thermal properties of specific objects. The CIELAB system is expressed as a color by three different color components: lightness (L*) and green-red (a*) and blue-yellow (b*) as shown in FIG. 3 . The a* axis shows green (-a*) to red (a*), and the b* axis shows blue (-b*) to yellow (b*). The pixel value varies depending on the radiometric resolution of the image acquired from remote sensing, such as 6, 8 or 16 bits.

LRV is an index measured based on a light source, and ranges from 0 (black, maximum light absorption) to 10 (white) based on a high correlation between color and light reflectance. Pure black has an LRV index of 0 and absorbs all light, while pure white has an LRV value of 100 and can reflect all incident light. However, since there is no object that completely absorbs or reflects light, an object with an LRV of 0 or 10 cannot be identified in reality. Since the color is ultimately calculated according to a mixed band of red-green, yellow-blue, and lightness, we can normalize the sun's reflectance, just like a white shirt lowers the body's heat load.

On the other hand, LRV can be derived as shown in Equation 1 by the British Standards Association (BS 8493 208).

4 is a graph of spectral irradiance according to a wavelength of a light beam. It will be described with reference to FIG. 4 . In the cool loop, the color corresponds to the region of the solar spectral wavelength that can be perceived by humans and corresponds to visible light of 400 nm to 700 nm. Therefore, changing the color is equivalent to changing the wavelength in visible light. The visible light wavelength corresponds to a very narrow wavelength band compared to the infrared wavelength, but corresponds to the region with the greatest solar intensity in the entire spectrum. LRV is derived from visible wavelengths and detects about 43% of the solar energy generated at wavelengths in the electromagnetic spectrum. On the other hand, about 52% of solar energy transferred as heat in the infrared region (IR, wavelength range: 70 nm to 250 nm) is not detected by LRV. Therefore, LRV cannot theoretically control the heating effect caused by the near-infrared (NIR) of solar radiation. However, infrared wavelengths are not easily controllable regions, but tuning colors in visible wavelengths is the simplest and most efficient tuning in the cool-loop region.

Referring back to FIG. 2 , the carbon emission is calculated from the energy consumption of the building calculated from the LRV ( S50 ). The higher the LRV value, the lower the energy consumption, the lower the energy consumption, the lower the carbon emission, and the higher the energy consumption, the greater the carbon emission. A specific calculation formula for calculating energy consumption and a specific calculation formula for calculating carbon emission can be empirically specified. The detailed energy consumption calculation formula is stored in the energy consumption calculation unit 150 . Energy consumption calculation formula includes LRV as a variable. The detailed carbon emission calculation formula is stored in the carbon emission calculation unit 160 . The carbon emission calculation formula includes energy consumption as a variable. As another example, carbon emissions may be calculated using LRV as a variable.

5 is a drone image taken according to an embodiment of the present invention. Referring to FIG. 5 , the drone image of FIG. 5 is a photo taken with an unmanned aerial vehicle at 1:00 PM on September 4, 2017. The photographed area is a photograph taken of the area around Kyungpook National University located in Daehyeon-dong, Buk-gu, Daegu Metropolitan City, which corresponds to 35°53' 16.6' - 09.62' north latitude and 128°3'11.25'E - 17.16'E longitude east. The area around the Kyungpook National University campus has a high density of buildings due to the rise in land prices and various commercial districts, and is an area with roofs of various shapes and colors. By using the visible light camera mounted on the unmanned aerial vehicle, the wide area real roof data was acquired. The camera has an image resolution of 4000Х3000 and identifies a subject with a total resolution of 12,00,00 pixels in a single scene. In addition, the camera provides a pixel size of 1.56 μm, so that high-resolution image quality such as field photos can be acquired in low-altitude flight. High-resolution visible light images can be utilized for precise LRV derivation as light reflectance through an 8-bit radiation resolution of 0-25. When determining the cool roof performance, 3D image mosaic was performed using the SfM technique for the raw data of the roof image, and data correction was performed. The data obtained from the visible light image extracted the CIELAB color system and derived the LRV for each roof color using the calculation formula for conversion to LRV.

FIG. 6 is an extract of 8 samples from the roof image included in the drone image of FIG. 5 . 7 shows eight roof image samples shown in FIG. 6 in drone images taken with a thermal imaging camera. 6 and 7 , the highest LRV index was found in the white roof of sample a (LRV: 91.4) and the lowest LRV index was in the black roof of sample g (LRV: 18.1). The dark gray color of sample b showed an LRV index closer to black than white (LRV: 37.4). Since most of the surface of the flat roof observed in the study area is covered with cement, it was composed of a light gray color at the time of initial construction. However, since it was left for a long time without proper roof maintenance, the surface of the roof was affected by various air pollutants such as pollutants, dust and soot. Thus, a dark gray roof tends to have a lower LRV closer to black. On the other hand, the blue roof of sample c was observed with the highest LRV index among chromatic roofs expressed in the CIELAB color system composed of green-red and blue-yellow color combinations (LRV: 65.4), and the roof with the lowest LRV index among chromatic colors. was identified on the surface of the red roof of sample e (LRV: 25.3). Although it is difficult to recognize a clear difference between the blue and red roofs, there was a big difference in the actual LRV distribution characteristics (red: 25.3, blue: 65.4). The dark brown roof of sample h (LRV: 27.8) shows a similar LRV distribution pattern as the black roof of sample g (LRV: 18.1). The green roof of sample f (LRV: 54.4) is a type of roof with urethane paint applied to waterproof the roof. It is a type of roof commonly observed in Korea. The low LRV index is confirmed as the main cause of the increase in cooling load in summer.

The surface temperature measured using the LRV and thermal imaging camera for each sample of FIG. 6 . Rooftop color LRV Red Green Blue

Temperature (

cool roof class a (white) 91.4 245 245 245 38 A b (gray) 37.4 167 164 163 53.5 C c (blue) 65.4 131 224 249 62.5 B d (cyan) 41.2 159 175 166 64.4 C e (red) 25.3 196 115 108 62.6 C f (green) 54.4 137 207 198 49.9 B g (black) 18.1 93 121 147 65 C h (brown) 27.8 149 141 146 69.1 C

로 일정하였으나 지붕의 색상에 따라서 표면 온도가 다양하게 분포하는 것이 확인되었다. 높은 LRV 특성을 가진 백색 지붕(LRV: 91.4)는 39.0°C의 가장 낮은 평균 표면 온도로 측정되었다. 흑색 지붕(LRV:18.1)의 경우 낮은 LRV를 가지며 상당히 높은 표면온도 65.0

로 측정되었다. The thermal image of the roof surface measured through the example of FIG. 7 was used as an index for verifying the LRV reflectivity, and the higher the surface temperature, the brighter yellow, and the lower, the darker the color. When acquiring the image, the outside temperature is 27.2

was constant, but it was confirmed that the surface temperature varied according to the color of the roof. The white roof with high LRV characteristics (LRV: 91.4) was measured with the lowest average surface temperature of 39.0 °C. The black roof (LRV: 18.1) has a low LRV and a fairly high surface temperature of 65.0

was measured with

8 is a graph showing eight samples of the roof image of FIG. 6 on the LRV (Light Reflectance Value) and surface temperature (Surface temperature) axes. As a result of the correlation analysis between LRV and surface temperature, the Pearson correlation coefficient (r) was -0.76 (Pearson's r = -0.76), indicating a strong negative correlation. As for correlation, correlation analysis quantitatively presents the surface temperature of the roof that changes based on various colors such as black, white, and blue.

9 is an image illustrating administrative districts according to an embodiment. The carbon emission of the present invention may be calculated according to each administrative district. As an example, it is possible to calculate the carbon emission of the entire Republic of Korea shown in Fig. 9 (a). As an example, the carbon emission in the Daegu metropolitan area shown in FIG. 9 (b) may be calculated.

On the other hand, in order to reduce energy consumption and reduce carbon emissions, the building further includes a notification unit 170 that notifies data about buildings in which energy consumption is detected above a predetermined range among buildings of the analyzed drone image to the outside, energy reduction measures may be taken.

이며 지붕의 표면온도와 외기온도의 최대 차이는 40.1

이며 최소 차이는 9

로 나타났다. 지붕 표면의 데이터는 일출 시간(05시 59분) 후 지붕 표면이 태양 에너지의 영향을 받기 시작하는 07시부터 태양 고도에 의거하여 표면 온도 상승에 영향을 미치는 15시까지 1시간 간격으로 취득할 수 있다. 색상이 있는 지붕과 백색 지붕의 변화 추세선 기울기에서 백색 지붕은 흑색 지붕과 비교하여 약 3배 낮은 수치가 나타났다. 따라서 백색 지붕은 외기의 영향을 비교적 적게 받으며 흑색, 청색, 청록색, 갈색 지붕은 상대적으로 받는 외기의 영향이 클 것으로 판단된다. 특히 청색 지붕은 흑색 지붕과 비교하여 상이한 색상의 특성을 가지지만 유사한 추세선 기울기 특성이 나타난다. 청색 지붕에서 변화 특성이 뚜렷하게 나타나는 것은 재질의 특성이 반영된 현상이며 공장에서 일반적으로 활용되는 샌드위치 판넬 재질을 사용하고 있으므로 열전도율이 뛰어난 특징을 가지고 있다. 따라서 태양 에너지의 변화에 따라서 민감한 표면 특성을 가지는 것으로 판단된다. 오전 07시는 일출 시각과 근접한 시간이며 지붕의 표면 특성은 뚜렷한 차이를 확인할 수 없었다. 태양 에너지에 따른 영향이 나타나지 않는 일몰 후, 일출 전 시간의 경우 지붕의 뚜렷한 표면 특성이 나타나지 않을 것으로 사료된다.Although the solar reflectance based on the color of the roof is one of the important indicators when determining the cool roof performance, the heat distribution of the roof surface has a high correlation with the heat emissivity of the surface finish or coating material. It is possible to determine the surface characteristics of individual roofs by deriving a time-series trend of pixel distribution changes in the captured drone images. In an embodiment of the present invention, when the solar altitude is the highest, the pixel value of the surface of the roof is maintained the highest, and at this time, some of the incoming solar energy is reflected, but mostly flows into the room through the roof. The change trend of the surface pixel values of individual roofs shows heterogeneous characteristics, but the difference in the width of change is a phenomenon that reflects the characteristics of the material of the roof surface. Therefore, it is possible to indirectly estimate the material properties by analyzing the indicators that affect the temperature rise of the roof surface in addition to the color through the evaluation of the change trend of the surface pixel values by time period. When looking at the trend of the change of pixel values over time, the time showing the maximum value is 13:00 and the value has been falling since then. When the roof surface temperature value is the highest, the outside temperature is 29

and the maximum difference between the surface temperature of the roof and the outside air temperature is 40.1

and the minimum difference is 9

appeared as Data on the roof surface can be acquired at 1 hour intervals from 07:00, when the roof surface begins to be affected by solar energy, after sunrise time (05:59) until 15:00, when the roof surface begins to be affected by solar energy, depending on the solar altitude. have. In the slope of the change trend line of the colored roof and the white roof, the white roof showed about 3 times lower value than that of the black roof. Therefore, it is judged that the white roof is relatively less affected by the outside air, and the black, blue, turquoise and brown roofs are judged to be relatively affected by the outside air. In particular, the blue roof has different color characteristics compared to the black roof, but similar trend line slope characteristics appear. The characteristic change in the blue roof is a phenomenon that reflects the characteristics of the material, and since the sandwich panel material commonly used in factories is used, it has excellent thermal conductivity. Therefore, it is judged to have a sensitive surface characteristic according to the change of solar energy. 07:00 am is the time close to the sunrise time, and there was no clear difference in the surface characteristics of the roof. It is thought that the apparent surface characteristics of the roof do not appear in the time before sunrise after sunset, when the effect of solar energy does not appear.

An embodiment of the present invention may also be implemented in the form of a recording medium including instructions executable by a computer, such as a program module to be executed by a computer. Computer-readable media can be any available media that can be accessed by a computer and includes both volatile and nonvolatile media, removable and non-removable media. In addition, computer-readable media may include both computer storage media and communication media. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Communication media typically includes computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave, or other transport mechanism, and includes any information delivery media.

The description of the present invention described above is for illustration, and those of ordinary skill in the art to which the present invention pertains can understand that it can be easily modified into other specific forms without changing the technical spirit or essential features of the present invention. will be. Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive. For example, each component described as a single type may be implemented in a dispersed form, and likewise components described as distributed may also be implemented in a combined form.

The scope of the present invention is indicated by the following claims rather than the above detailed description, and all changes or modifications derived from the meaning and scope of the claims and their equivalents should be interpreted as being included in the scope of the present invention. do.

Claims (10)

Acquiring a drone image including a roof image according to each visible light of a plurality of buildings with a drone;
extracting a light reflectance value (LRV) defined for each color from the color constituting the respective roof image;
calculating energy consumption of each of the plurality of buildings from the LRV; and
A method of calculating carbon emissions using a roof image of a drone image, comprising the step of calculating carbon emissions from the calculated energy consumption. According to claim 1,
A method of calculating carbon emissions using a roof image of a drone image that calculates that the energy consumption is lower as the value of the LRV is higher. According to claim 1,
A method of calculating carbon emissions using a roof image in which the drone image is taken for each administrative district, and the carbon emission is calculated according to the administrative district. According to claim 1,
The step of acquiring the drone image includes acquiring a plurality of drone images according to a plurality of different times for the same roof,
Further comprising the step of determining the material of the roof from the change in pixel values constituting the roof image in the plurality of drone images for the same roof and time information at which the drone image is captured,
In the step of calculating the energy consumption of each of the plurality of buildings, a method of calculating carbon emissions using a roof image of a drone image for calculating the energy consumption from at least one of a reflectance or an emissivity of the material of the roof and the LRV. According to claim 1,
A method of calculating carbon emissions using a roof image of a drone image informing the outside of data about a building in which energy consumption is detected above a predetermined range among the plurality of buildings of the drone image. a data receiver for acquiring drone image data including a roof image according to each visible light of a plurality of buildings by drone photography;
a roof determination unit for determining the roofs of the plurality of buildings from the drone image;
an LRV extraction unit for extracting a light reflectance value (LRV) defined for each color from each color constituting the roof of the plurality of buildings;
an energy consumption calculation unit calculating energy consumption of each of the plurality of buildings from the LRV; and
A carbon emission measurement system including a carbon emission calculation unit for calculating carbon emission from the calculated energy consumption. 7. The method of claim 6,
The energy calculation unit, a carbon emission measurement system that calculates that the higher the value of the LRV, the lower the energy consumption. 7. The method of claim 6,
The drone image is composed of a plurality, and includes an integrator for integrating the plurality of drone images, and the integrator integrates the plurality of drone images according to an administrative area,
The carbon emission calculation unit is a carbon emission measurement system for calculating the carbon emission according to the administrative district. 7. The method of claim 6,
The data receiver receives a plurality of drone images according to a plurality of different times for the same roof,
The roof determination unit determines the material of the roof from a change in pixel values constituting the roof image in the plurality of drone images for the same roof and time information at which the drone image is captured,
The energy consumption calculation unit is a carbon emission measurement system for calculating the energy consumption from at least one of reflectance and emissivity according to the material of the roof and the LRV. 7. The method of claim 6,
The carbon emission measurement system further comprising a notification unit informing the outside of data about a building in which energy consumption is detected above a predetermined range among the plurality of buildings of the drone image.

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