WO2018220263A1

WO2018220263A1 - Search engine to search natural resource

Info

Publication number: WO2018220263A1
Application number: PCT/FI2018/050365
Authority: WO
Inventors: Tuomo KAURANNE; Jari Kinnunen; Vesa Leppänen; Jussi Peuhkurinen
Original assignee: Oy Arbonaut Ltd.
Priority date: 2017-06-02
Filing date: 2018-05-16
Publication date: 2018-12-06

Abstract

A search engine to search natural resource is disclosed. The search engine searches natural resources from a database or another kind of data structure comprising information on natural resources arranged into units that can be searched. Based on the search result the owner of the natural resource can be searched and identified. Using the search engine the user can search for natural resources that are ofa particular interest even if the proprietor has not shown active interest to sell the natural resource by making a sales offer.

Description

SEARCH ENGINE TO SEARCH NATURAL RESOURCE

DESCRIPTION OF BACKGROUND

The following disclosure relates to identifying natural resource of the best suitability for an industrial process. Particularly, the disclosure relates to a system and method for identifying natural resources and a process for facilitating the identification.

Search engines are known to mankind. They are used to search desired information from a large amount of information contents. Typically, they have a user interface or machine-to-machine interface to enter the desired properties of the information to be searched. From this input, they produce a search vector that is used to query or filter the desired piece of information from a large sea of information that is, to most part, uninteresting to the desired purpose of the search. Searching natural resources is complicated to search engines known today since the information about their properties interesting to industries are not available in searchable format in the searchable Internet or other media. Thus, information about natural resources need to be obtained and organized into a data structure, database or similar in order to facilitate the searching. In this document the word "Database" refers to any organized structure of data that is stored to a computer readable medium.

The database can be constructed by collecting data. This is often done using sensing methods. Sensing may produce different kinds of datasets, like three dimensional point clouds, raster images or measurements. These sensing results may be taken from individual targets or covering smaller or wider areas. In some cases, the targets may be individual trees, plants, units or groups of the above. Three-dimensional point clouds are one possible output commonly used in sensing. Point clouds can be produced using multiple techniques, for example, LiDAR, photogrammetry and radargrammetry . Similarly, there are many methods to collect measurements images that are commonly known to experts in the field.

Photogrammetry is the science of making measurements from photographs, also called images. Stereophotogrammetry is a methodology of Photogrammetry where group of two or more images taken of the same target are analyzed. The images have to be taken from different viewpoints, presenting the objects from different angles, captured by the observing sensors at different locations. Corresponding features are identified in different images and their relative location on the image are interpreted, together with the sensor location and attitude to calculate the 3D location of the objects on the real life. The sensor locations may be given to the algorithm or, alternatively, deduced from the analysis. The locations of the matching points may be turned to a point cloud, or to a set of measurements. These, in turn, may be tied to a spatial reference to be used with other location- based information.

LiDAR, known also as laser scanning, has been used for forest inventories approximately since 1990' s. LiDAR is an active instrument that uses laser ranging, combined with devices measuring position and attitude of the sensor, to produce 3D location measurements of objects. The sensor emits a laser beam to a known direction from a known position and records the distance to surfaces where the beam is reflected back. Additionally, LiDAR may have capability to record the intensity of the returning signal, indicating the reflectivity and size of the reflecting surfaces. Some LiDARs work on multiple laser wavelengths, allowing the analysis of intensity values in different bands as additional information about the target. The laser beam may be projected to the object through a mirror or prism system or other kind of optical setup (the "LiDAR Optic") that causes the laser beam to scan the target area, recording the precise direction where the beam was sent each time to allow construction of the three- dimensional measurements. Alternatively, the optic may be solid, sending an array of beams without scanning function. On some LiDAR' s, the sensor, sensor array or any of them, combined to a scanning system, is moved to add a dimension to the measurements.

Traditionally, LiDAR has been used to produce attributes to areas of land. For example, LiDAR-derived attributes have been assigned to timber stands, making management or inventory units. Because of its capability to measure vegetation height and canopy densities, LiDAR has been widely accepted in forest inventory purposes. LiDAR inventories represent a significant economic value as it reduces the need for fieldwork.

Radargrammetry is the technology of extracting geometric object information from radar images. The output of the radargrammetric analysis may be for example a geometric three dimensional point cloud. Like stereophotogrammetry and LiDAR, also radargrammetry can be used from airborne or satellite, ground and water vessel platforms.

Human production processes, including, for example, industrial processes, often require raw materials. Natural resources may be used to provide for these raw material needs. In practice, there tend to be variations in the type and quality of the raw materials appearing in the nature or in managed production areas. Due to this variation, some sources of raw material are more suitable to the corresponding production processes than others. However, due to the lack of accurate information and humankind' s inability to properly process such information even if it might exist, suboptimal raw material ends up to the processes. This, in turn, causes many processes to use more of the raw materials than necessary, due to waste of insufficient quality of input, or to run with less than optimal efficiency. An example of this is a sawmill, producing high quality furniture panels from large pine butt logs. Due to forest's natural feature of often producing mixed species and due to forest tree's tendency to produce large knots in some conditions, a large proportion of the raw material acquired from an average logging site will not be suitable for the production. The efficiency of the production strongly depends on the mill operator' s ability to find and acquire raw material resources with high content of knot free pine butt logs. With today's processes where often timber statistics are produced to the land owner, and the land owners are contacting the timber users when they feel the urge to sell some timber, a lot of suboptimal stands are getting to the processes. At the same time, the resources that would be well suitable for one timber user, get to the other production facilities, where they, in turn, are not optimal.

There have been developments to establish trading sites that allow the sellers and buyers to communicate in a marketplace, to find each other and request proposals for given raw material units, leading to some dealing on the material. However, these systems lack the ability to search the entire raw material base, and depend on both buyers and sellers being active. A system that allows searching the raw material base regardless of the ownership would allow active buying, getting proposals also to the natural resource owners who do not know they may have some material of desired qualities. This would be also beneficial for these owners since they would become informed of the desired product they have and could get an option to sell or not sell the resource. Thus, there is a need for a method and system for providing improved raw material information acquirement . SUMMARY

A search engine to search natural resource is disclosed. The search engine searches natural resources from a database or another kind of data structure comprising information on natural resources arranged into units that can be searched. Based on the search result the owner of the natural resource can be searched and identified. Using the search engine the user can search for natural resources that are of a particular interest even if the proprietor has not shown active interest to sell the natural resource by making a sales offer .

In an embodiment a method for searching natural resource is disclosed. The method comprises receiving a request, wherein the request comprises at least one desired property of natural resource in accordance with a need of the user making the request; generating a first query; transmitting the first query to a first database; and receiving a first set of results, wherein the first set of results comprises at least one identification of a unit comprising a natural resource in accordance with the received request. This arrangement is beneficial as it provides possibility to receive an indication of a unit of natural resource without explicit sales offer or similar. Furthermore, it provides a possibility to optimize the harvesting equipment movements so that the movements can be minimized and the cost reduced.

In an embodiment the method further comprises generating a second query, wherein the second query comprises at least one received identification of a unit; transmitting the second query to a second database; and receiving a second set of results. This is particularly beneficial. In another embodiment the second set of results comprises an owner data of at least identified unit. This is particularly beneficial as the combination provides a possibility to contact the natural resource owner directly also in cases where the owner is not actively selling the natural resource as he may be unaware of the need of users. The owner data can be arranged as an anonymous identifier if privacy laws require that.

In a further embodiment the first set of data comprises natural resource data of each unit. This is particularly beneficial as each of the units may comprise desired and other raw materials. Thus, the acquirer may estimate how big portion is desired and at which price he could sell the other raw materials.

In another embodiment the first set of data comprises additional information for each unit. This is particularly beneficial when considering the necessary movements of harvesting machines and which kind of harvesting machine is needed.

In a further embodiment the method further comprises generating the first database. This is beneficial as a particularly for the purpose generated database facilitates better queries. The generating further comprises: sensing data about raw material; providing support data; acquiring reference data; processing unit segments using the sensed data, provided support data and acquired reference data; predicting the raw material properties to the area of the sensing data using the sensed data, provided support data and acquired reference data; and summarizing the processed unit segments and predicted raw material properties in order to provide raw material supply data. This is beneficial as processing the complicated requests may be facilitated by the particular generating method which is capable of predicting the raw material properties as they evolve over the time. In an embodiment the natural resource is forest. In a further embodiment the unit is a timber stand. In another embodiment the method is used in mechanical or chemical forestal industry.

In a further embodiment the method described above is implemented as a computer program. In another embodiment an apparatus is disclosed. The apparatus comprises at least one processor configured to execute computer programs and at least one memory configured to store computer programs and related data. The apparatus is configured to perform a method as described above.

A benefit of the method and system as described above is that it provides possibility to acquire raw material from a natural resource. The provided solution works very well both to buying a natural resource from external providers and acquiring raw material from self- owned natural resources.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are included to provide a further understanding of the search engine to search natural resource and constitute a part of this specification, illustrate embodiments and together with the description help to explain the principles of the search engine to search natural resource. In the drawings :

Fig. 1 a block diagram of an example embodiment of the search engine to search natural resource,

Fig. 2 an example of a method of searching using a search engine is disclosed,

Fig. 3 an example of a process of constructing searchable database or data structure is disclosed. DETAILED DESCRIPTION

Reference will now be made in detail to the embodiments, examples of which are illustrated in the accompanying drawings .

In the following description a search engine and respective data organization method is disclosed. Acquiring information on natural resources has been done in the industry. For example, in forest management processes, information about the forest stands have been collected. Similarly, in many countries, national forest inventory includes production of continuous information layers about forests. A person or team skilled in the art of natural resource inventory is capable of producing such information. In one embodiment, a raster image may be used to store the information, each pixel presenting the values of attributes of interest. In another embodiment, a set data may be used, where geometries are presenting the extent of each information unit and attributes are indicating the values of interest.

In the following description wood based raw materials are discussed because use of wood based raw materials is increasing due to increasing human population on earth and the wood based materials' favorable impact to environment, when compared to other alternatives. The embodiments disclosed in this disclosure are especially suitable for searching wood based raw materials, since their physical appearance allows data acquisition with sensing methods, facilitating production of the database of the raw material. These wood based raw materials include timber, pulpwood, firewood, energy wood, tree stems, poles, fruits of trees, branches, tops, leaves, bark, stumps or roots of trees, cellulose biomass produced on agriculturally managed crops or other organic material received from plants with woody stem. In the following description the natural resource may be any combination of the wood based raw materials that are discussed above. One embodiment of particular interested is wood for forest industry, such as timber and pulp wood mentioned above or similar.

One practical approach of acquiring information on natural resources is a process where remote sensing is utilized to acquire the information. In this method, remotely sensed data, covering the area of interest, may be connected to some measurements of the natural resource, making a model to predict the properties over the entire area of interest.

Another approach of acquiring information on natural resource is a process where field measurements are taken as a sample over the area of interest, and statistics of the entire area of interest are produced. This invention does not state a given method on how the information has to be acquired.

For querying the data over a large land base, a limited number of units containing the information has to be acquired. To do this, a segmentation method is applied in this invention. In the segmentation, relatively uniform units, in terms of the material of interest, are produced. This phase may be done using either manual or automatic segmentation or any combination of them.

To be able to query units of greatest interest, the properties of interest need to be connected to these segments. This can be done, for example, using statistics generated from the information layers to the segments, representing the content and properties of the material of interest inside each segment.

Having a data of segments with information of interest connected to each of the segments, it is possible to use querying techniques to find the most suitable segments for the given process. When a query is made, it is possible to return the units of the greatest interest to the user. In one embodiment, these properties may include a distance or transport cost on a transport network to the use location of the raw material. Further on, in one embodiment, these properties may include such properties as proportion of knot free butt logs of a given timber species. Further, in on embodiment the properties may include the volume and quality of corn in a corn field at some point of growth season.

In figure 1 a block diagram of a search engine to search natural resource is presented. The search engine is implemented as a service running on a computer 100. The computer 100 is typically a server, but may also be desktop computer, cloud computing facility, a mobile phone or other similar distributed computing facility. The server 100 comprises at least one processor 101 and at least one memory 102. In the example the server 100 is connected to a first database 104 and a second database 105.

The first database 104 comprises natural resource data that is divided into a set of small units that comprise information about the natural resources within the unit and additional information. Each of the units is tied to a map and corresponds with the respective location in the nature. The first database 104 is shown as a separate component in the example of figure 1, however, it may also be incorporated with the server 100. The server 100 is configured to generate and transmit queries to the first database and receive information representing the natural resources on the unit. The shape and size of units may be chosen according to the need and may be regular or irregular. For example, they may be squares, rectangles or hexagons of the same size or, alternatively shapes of different forms and sizes.

The second database 105 is optional. It comprises information about land ownership. The ownership is divided into predetermined pieces of land that may be of any shape and size. These ownership information comprises the location of the land so that it can be mapped against the set of the first database 104. The mapping between the first and second databases needs not to be even as the property borders usually do not follow the set. Thus, some of the units may divide among two or more owners. Thus, the owner, or owners, of selected units may be derived from the second database 105. Even if units may have more than one owner typically only one owner is of more importance. The second database 105 may include the contact information of the owner, however, it is possible to use owner identification that does not reveal the identity of the owner because of the privacy legislation. In such case it is possible to check the owner and the contact information from a further external system.

The server 100 is accessed by using a terminal device 103 that may be any conventional terminal device. These include, for example, ordinary computers, laptops, mobile phones and similar. The server 100 accessed may include all of the needed functionality and the user interface shown on the terminal device 103 may be, for example, a webpage or an user interface application that can be used for transmitting requests.

In figure 2 an example of a method of searching using a search engine is disclosed. In the method first a search request is received, step 200. To be able to search raw material it is important to determine the needs of the user. This is typically done, for example, by submitting the needs to a form on a webpage or application. The needs maybe communicated as a file produced by such application and they may be expressed as a value matrix.

From the received request a query for a database or other data source is generated, step 201. The query may be, for example, vector formed based on the needs of the user, later on in this document, the "Need". Typically the need explicitly expresses what the particular user needs for. The query may be executed to the raw material supply data, such as the first database of figure 1, to extract the units with the most desirable properties. The generated query is then transmitted to the first database, step 202.

As a response a set of first results is received, step 203. The first database processes the query and provides the set of results indicating where the requested raw material matching with possible additional search parameters, such as distance from the location of the need or accessibility, can be found. The results may be provided to the user, for example, in a form of the map or as a list of property numbers to which the units on which the returned raw material resources belong to. The method, at its simplest, may be terminated here. Then, with the received information the user has to find out who owns the properties so that a purchase can be made. The first set of results does not limit to the locations of the units found but also include the information of the contents of returned units. Typically each of the units include desired and non-desired raw material. The desired raw material is of high quality and fits the need of the user. Non- desired raw material may be of lower quality or completely unsuitable quality. Typically the non- desired raw material is sold to somebody else in order to lower the desired raw material cost. The first set of results includes information on the raw material so that the user may inspect the distribution between the desired and non-desired raw material and estimate the price of the non-desired raw material. Then it is possible to calculate how much the user is willing to invest for raw material as whole so that the price of the desired raw material is on acceptable level. The investment may comprise costs related for buying the raw material or acquiring the raw material from own raw material resource. In such case the acquiring cost means, for example, harvesting, transporting and other similar costs.

In more sophisticated embodiment a second query is generated, step 204. In a typical embodiment the second query comprises a list of property identification codes or similar. The second query is then transmitted to the second database, step 205. As a response contact information of the property owner may be returned, step 206. It should be noted that automatic processing of contact information might not be allowed in all countries and the privacy laws must be taken into account. Furthermore, it is possible that there is a third database that includes a list of property owners that authorize the service to use their contact information in the service when such an authorization is needed because of the law.

Figure 3 discloses an example of a process of constructing search database. The raw material base, box 300, is a source of natural resource that has the capability to fill the raw material user's need. Typical examples of raw material base include a forest of natural or human-induced origin, cornfield, coffee-, tea-, citrus or other plantation, or other similar natural resource that can be used in industry. However, in many raw material bases, the quality and content of the raw material varies. In this disclosure, the raw material base is considered to be divided to units that are areas to be practically processed as one operation. Some units of raw material are more suitable to the Need than others. For example, in case of high quality pine lumber production, the proportion of pine in the forest Units varies. Additionally, among pine trees, there are ones that have the qualities desired for high quality pine lumber production. Additional to the desired raw material, there may be varying amounts of other products in the units. These products may have value, but may not be desired by the Production. For example, a unit of roundwood forest may have pine trees yielding high quality pine logs, but also broadleaved trees, other coniferous trees and pines that do not yield raw material to high quality pine lumber when used in the Production. These materials may be called "Undesirable raw materials" or "Side products". The raw material base is the source of all information and the process derives information from the natural resource. The usability, for example possibility to sell, of side products may be important for calculating the overall cost of acquiring the raw material. Thus, desirable raw material should be understood to be raw material that the acquirer needs for own use. Side products, and particularly the sale of side products, may be necessary in order to reduce overall costs.

Sensing, box 301, is a group of methodologies used to acquire information from a target via sensors. The sensors use information transmitted from the target to the sensor and measure some properties of the information. This information may be for example electromagnetic radiation, sensed passively as recording the radiation from the Target or actively by sending radiation to the Target and measuring the radiation emitted back to the sensor. LiDAR is an active sensor that sends laser pulses to the target, knowing the sensor attitude and location with high precision. The reflected pulse is recorded measuring the distance to the Target, the attitude and location of the sensor and potentially, the intensity of the reflected radiation. A three-dimensional point cloud may be made from the recorded information by projecting the reflections to a spatial reference system. Passive sensing methodology includes for example cameras recording the radiation from the target to the camera in a pixel array. The recording may be done simultaneously in multiple bandwidths of electromagnetic radiation, yielding for example, multispectral or hyperspectral imagery.

Providing other supporting data, box 304, may be data about land ownership, infrastructure or drainage condition of the target area, operability or seasonal operability of the target area, tree species composition of a forest, soil type and condition of an agricultural Unit or some other information that may be usable for making decisions or producing Unit Metrics. The supporting data, for example the infrastructure and seasonal operability, may be of particular interest because even if the raw material is of suitable quality it may not be accessible during all seasons or conditions and, thus, it is not interesting when the season is not correct and the need of the user has to be fulfilled before the suitable season starts.

Reference data, box 303, is data acquired from the raw material base, quantifying and qualifying the raw material. The reference data may cover parts of the sensing data area. The reference data may be used to make models between the sensing data and the raw material properties. This allows prediction of the raw material properties to the entire raw material Base or to part of it. Additionally, and optionally, other supporting Data may be used to help in the prediction of the raw material properties. In one embodiment of the innovation, the reference Data may contain forest plot measurements, where circular plots have been selected from the raw material Base, plot locations measured with GNSS technology and timber characteristics collected from the plots. In another embodiment, the reference data may be collected from crop harvesting machines recording the yield and quality of the crop being harvested, tied to location.

Unit segmentation, box 302, processes units that are practical for management of data but also for querying it. Practical units are important to successful analysis of the raw material base. Typically unit is an area that is practical to operate at once; for example, a forest stand or block that is practical to harvest as one operation or an agricultural field parcel.

This disclosure assumes use of a method to automatically produce units from sensing data. There are multiple methods to automatically produce such units, one of them being called "Automatic Segmentation". Automatic segmentation methods are known to mankind and can be performed by professionals specialized in the matter. In one embodiment, the automatic segmentation is an automatic method to produce forest stands. Forest stands are pieces of relatively uniform forest that are practical to be operated as one unit.

Attribute values on the Inventory Set can be used as an input or as supplementary information in production of the Units. An example of a segmentation method can be found in publication: V. J. Leppanen, T. Tokola, M. Maltamo, L. Mehtatalo, T. Pusa, J. Mustonen. 2008. "Automatic Delineation of Forest Stands from LiDAR Data. "

Prediction Process, box 305, is a process that receives the reference data and sensing data and predicts the raw material properties to the area of the sensing data. Additionally, and optionally, Other Supporting Data may be used to help in the prediction of the Raw Material properties. In one embodiment of the innovation, the prediction process is used to predict timber characteristics to a set that covers the entire Raw Material Base or parts of it "Inventory Set". The set may be formed of cells of regular shape, like a square or a hexagon, or of irregular shape. In the end of the Prediction Process, each set cell has predicted timber characteristics associated to it. A set cell is called "Analysis Cell" when it has been linked to a table of attributes. An example of the prediction methodology can be found from a publication: Junttila, Virpi; Kauranne, Tuomo; Leppanen, Vesa: 2010. "Estimation of Forest Stand Parameters from Airborne Laser Scanning Using Calibrated Plot Databases" Forest Science, Volume 56, Number 3, 1 June 2010, pp. 257- 270 (14) .

Finally the processed information is summarized, box 306. The prediction process has produced characteristics to the raw material base. Unit segmentation has produced units. Summarizing Information to Units summarizes the characteristics that may be associated to Analysis Cells, joining the summary of all cells inside one unit to the attributes of that unit. Thus, a raw material supply data, box 307, has been achieved.

The above mentioned method may be implemented as computer software which is executed in a computing device able to communicate with a mobile device. When the software is executed in a computing device it is configured to perform the above described inventive method. The software is embodied on a computer readable medium so that it can be provided to the computing device, such as the server 100 of figure 1.

As stated above, the components of the exemplary embodiments can include computer readable medium or memories for holding instructions programmed according to the teachings of the present inventions and for holding data structures, tables, records, and/or other data described herein. Computer readable medium can include any suitable medium that participates in providing instructions to a processor for execution. Common forms of computer-readable media can include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, any other suitable magnetic medium, a CD- ROM, CD±R, CD±RW, DVD, DVD-RAM, DVD1RW, DVD±R, HD DVD, HD DVD-R, HD DVD-RW, HD DVD-RAM, Blu-ray Disc, any other suitable optical medium, a RAM, a PROM, an EPROM, a FLASH-EPROM, any other suitable memory chip or cartridge, a carrier wave or any other suitable medium from which a computer can read.

It is obvious to a person skilled in the art that with the advancement of technology, the basic idea of the search engine to search natural resource may be implemented in various ways. The search engine to search natural resource and its embodiments are thus not limited to the examples described above; instead they may vary within the scope of the claims.

Claims

1. A method for searching natural resource which method comprising:

receiving a request, wherein the request comprises at least one desired property of natural resource in accordance with a need of the user making the request; generating a first query;

transmitting the first query to a first database; and

receiving a first set of results, wherein the first set of results comprises at least one

identification of a unit comprising a natural resource in accordance with the received request,

wherein the natural resource is forest.

2. The method according to claim 1, wherein the method further comprises

generating a second query, wherein the second query comprises at least one received identification of a unit;

transmitting the second query to a second

database; and

receiving a second set of results.

3. The method according to claim 2, wherein the second set of results comprises an owner data of at least identified unit.

4. The method according to any of preceding claims 1 - 3, wherein the first set of data comprises natural resource data of each unit.

5. The method according to any of preceding claims 1 - 4, wherein the first set of data comprises additional information for each unit.

6. The method according to any of preceding claims, wherein the method further comprises generating the first database.

7. The method according to claim 6, wherein the generating further comprises:

sensing data about raw material; providing support data;

acquiring reference data;

processing unit segments using the sensed data, provided support data and acquired reference data;

predicting the raw material properties to the area of the sensing data using the sensed data, provided support data and acquired reference data;

summarizing the processed unit segments and predicted raw material properties in order to provide raw material supply data.

8. The method according to any of preceding claims 1 - 8, wherein the unit is a timber stand.

9. The method according to any of preceding claims 1 - 8, wherein using the method in mechanical or chemical forestal industry.

10. Computer program comprising computer program code, which is configured to cause a method according to any of preceding claims 1 - 9 when executed in a computing device.

11. An apparatus for providing a search engine comprising :

at least one processor (101) configured to execute computer programs;

at least one memory (102) configured to store computer programs and related data;

wherein the apparatus is configured to perform a method according to any of preceding claims 1 - 9.