1. Moving from \u201cvisible\u201d to \u201cunderstandable\u201d is the key to the qualitative change of the industry. After decades of development, we have successfully solved the problem of resolutions ranging from hundreds of meters to sub-meters and revisit cycles from days to hours. However, objectively speaking, although users have accumulated a large amount of data, the proportion of actual value generated on the application side is still low. In the next few years, the core competitiveness of the development of the remote sensing satellite industry may no longer be the number of satellites or the resolution, but the ability to transform remote sensing data into decision-making information. If the remote sensing data industry wants to achieve great development, the application threshold cannot be too high. Just like Jack Ma said in an interview with the “Wealth Life” program in 2003, “I must try any program they write. If I don’t know how to use it, throw it away. I said 80% of people are as stupid as me and won’t use it. I hope I can say it without reading it.” “You can use it without reading anything in the manual.” This saying is not only applicable to the Internet industry, but also applies to the remote sensing satellite industry. In the future, whoever can allow non-professional users to ask questions in natural language, such as “Is there any illegal construction in the area I am responsible for this week?” and get accurate answers, will be able to occupy the high end of the value chain.<\/p>\n
2. Data sharing and standard unification are prerequisites for industry growth. In the context of the explosive growth of remote sensing data and the marketization of the remote sensing industry, the necessity of standardization and standardization of satellite image data products and services is no longer the same as before. At present, everyone has their own data formats and interface standards, and the user integration and use costs are extremely high. For example, a province needs to integrate data from more than a dozen satellites to monitor geological disasters. As a result, the satellite data interfaces are different. Just writing the adaptation code consumes half of the project budget. This has created a very embarrassing situation. Remote sensing technology can obviously solve many problems, but users (especially users in traditional industries) are dissuaded by the difficulty of integration after trying it once, making it difficult for the entire market to develop. Moreover, many units regard “inconsistent data formats” as a moat and would rather “reinvent the wheel” than unify standards, causing the entire industry to be trapped in a “shallow water zone” where it is difficult to make major breakthroughs. Only after unified standards will users be more willing to embed remote sensing data into their business systems, and the pie in the entire market will be bigger.<\/p>\n
3. \u201cDownstream drives upstream\u201d is the only way for the development of commercial remote sensing. In the past, when people talked about the remote sensing satellite industry, they basically talked about “building constellations and launching satellites.” As a result, the satellites were built and put into the sky, but the market failed to keep up. In the past few years, this lesson has been painful, but many companies are still following up. In the future, the healthy development path of the remote sensing industry must be that the demand side defines the supply side. For high-frequency scenarios such as agricultural insurance, carbon sequestration monitoring, infrastructure physical examinations, and urban illegal construction inspections, we must first understand what the real needs of users are, and then find out the users’ willingness to pay and unit price, and then work backwards to determine the satellite scale and constellation design. For example, for government customers, their biggest pain point is not timeliness, but accuracy. Things released by the government must be accurate. Once the data is wrong, it will seriously affect the authority and credibility of the government. If all they get is a bunch of labels with suspected patterns and a confidence level of 70%, then each department still needs to do manual review by itself. For them, time and capital costs are not saved. Moreover, for the government’s routine monitoring, such as quarterly farmland changes, annual land change surveys, etc., there are fixed reporting nodes. Getting the results 3 days in advance is not as big as getting the results 2 weeks in advance. However, if the accuracy is insufficient and leads to rework, missing the node will be an accident. For commercial companies, the more hidden risk is that once they are “blacklisted” due to data quality issues in a national-level project, they will basically say goodbye to this track. In the next two to three years, those companies that are still telling the story of “highest resolution and most satellites” may find it increasingly difficult to raise funds. Only companies that can truly come up with a few vertical scenario cases of “customers renewing contracts every year, increasing unit price without subsidies” are likely to truly survive. In the second half of commercial remote sensing, the competition is definitely not about how many mirrors someone has placed in the sky, but about which corner on the ground shines a beam of light that can solve specific problems.<\/p>\n","protected":false},"excerpt":{"rendered":"
1. Moving from \u201cvisible\u201d to \u201cunderstandable\u201d is the key to the qualitative change of the industry. After decades of development, we have successfully solved the problem of resolutions ranging from hundreds of meters to sub-meters and revisit cycles from days to hours. However, objectively speaking, although users have accumulated a large amount of data, the […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":"","_links_to":"","_links_to_target":""},"categories":[2],"tags":[],"class_list":["post-25281","post","type-post","status-publish","format-standard","hentry","category-news"],"acf":[],"_links":{"self":[{"href":"https:\/\/starpath.global\/blog\/wp-json\/wp\/v2\/posts\/25281"}],"collection":[{"href":"https:\/\/starpath.global\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/starpath.global\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/starpath.global\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/starpath.global\/blog\/wp-json\/wp\/v2\/comments?post=25281"}],"version-history":[{"count":0,"href":"https:\/\/starpath.global\/blog\/wp-json\/wp\/v2\/posts\/25281\/revisions"}],"wp:attachment":[{"href":"https:\/\/starpath.global\/blog\/wp-json\/wp\/v2\/media?parent=25281"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/starpath.global\/blog\/wp-json\/wp\/v2\/categories?post=25281"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/starpath.global\/blog\/wp-json\/wp\/v2\/tags?post=25281"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}